aid stringlengths 9 15 | mid stringlengths 7 10 | abstract stringlengths 78 2.56k | related_work stringlengths 92 1.77k | ref_abstract dict |
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1907.02584 | 2953945697 | We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes. We show that class prototypes, obtained using either an encoder or through class specific k-d trees, significantly speed up the the search for counterfactual instances and ... | Incorporating prototypes in the objective function leads to more interpretable counterfactuals. We introduce two novel metrics which focus on local interpretability with respect to the training data distribution. This is different from @cite_26 who define an interpretability metric relative to a target model. @cite_23 ... | {
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"We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretabil... |
1907.02684 | 2952015385 | Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating constituent and dependency formal representations into head-driven phrase structure. Then two parsing... | In the earlier time, linguists and NLP researchers discussed how to encode lexical dependencies in phrase structures, like lexicalized tree adjoining grammar (LTAG) @cite_18 and head-driven phrase structure grammar (HPSG) @cite_2 which is a constraint-based highly lexicalized non-derivational generative grammar framewo... | {
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"In this paper we present a general parsing strategy that arose from the development of an Earley-type parsing algorithm for TAGs (Schabes and Joshi 1988) and from recent linguistic work in TAGs (Ab... |
1907.02684 | 2952015385 | Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating constituent and dependency formal representations into head-driven phrase structure. Then two parsing... | Meanwhile, since HPSG represents the grammar framework in a precisely constrained way, it is difficult to broadly cover unseen real-world texts for parsing. Consequently, according to @cite_60 , many of these large-scale grammar implementations are forced to choose to either compromise the linguistic preciseness or to ... | {
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"We present a simple and intuitive unsound corpus-driven approximation method for turning unification-based grammars, such as HPSG, CLE, or PATR-II into context-fre... |
1907.02844 | 2954922979 | Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic distances. However, such methods operate on the ambient dimensionality, and are therefore fragile to noise dimensions. We developed an unsupe... | Nonlinear manifold learning approaches, such as Isomap @cite_18 , Laplacian eigenmaps @cite_0 and UMAP @cite_19 , are designed to preserve geodesic distances, and even directly estimate them. Specifically, they follow a three-step process. First, they estimate geodesic distances in the original manifold. This is done b... | {
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"Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geom... |
1907.02844 | 2954922979 | Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic distances. However, such methods operate on the ambient dimensionality, and are therefore fragile to noise dimensions. We developed an unsupe... | One of the most widely used methods for nonlinear dimensionality reduction is Isomap @cite_18 . Isomap is one of the few manifold learning algorithms that has theoretical guarantees for correctly estimating the manifold under certain assumptions @cite_23 . In the case of many noisy dimensions, however, Isomap fails to ... | {
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"Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality redu... |
1907.02844 | 2954922979 | Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic distances. However, such methods operate on the ambient dimensionality, and are therefore fragile to noise dimensions. We developed an unsupe... | Approximate nearest neighbors algorithms, such as FLANN @cite_20 , approximate nearest-neighbors in high-dimensional data sets, typically by building binary space-partitioning trees, such as @math -d trees. These algorithms are designed to estimate the distances in the observed high-dimensional space. When the true man... | {
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"For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding... |
1907.02844 | 2954922979 | Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic distances. However, such methods operate on the ambient dimensionality, and are therefore fragile to noise dimensions. We developed an unsupe... | This work is inspired by, and closely related to, random projection trees for manifold learning @cite_17 and vector quantization @cite_14 . The main differences between our approach and theirs is (1) that they use random splits, rather than optimizing the splits; and (2) they use a single tree, whereas URerF uses a for... | {
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"A simple and computationally efficient scheme for tree-structured vector quantization is presented. Unlike previous methods, its quantization error depends only on the intrinsic dimension of the d... |
1907.02844 | 2954922979 | Geodesic distance is the shortest path between two points in a Riemannian manifold. Manifold learning algorithms, such as Isomap, seek to learn a manifold that preserves geodesic distances. However, such methods operate on the ambient dimensionality, and are therefore fragile to noise dimensions. We developed an unsupe... | Finally, most closely related to our method are existing unsupervised random forest methods, the most popular of which is included in Adele Cutler's RandomForest R package @cite_33 . It proceeds by generating a synthetic copy of the data by randomly permuting each feature independently of the others, and then attempts ... | {
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"A random forest (RF) predictor is an ensemble of individual tree predictors. As part of their construction, RF predictors naturally lead to a dissimilarity measure between the observations. One can also define an RF dissimilarity ... |
1812.08247 | 2904996883 | Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. deepfakes'. Levera... | Since their introduction in 2014 @cite_6 , GANs have quickly become an extremely valuable tool in a range of computer vision applications. At a high level, the concept of a GAN is that two networks are trained to compete with one another. The generator' network is trained to produce artificial imagery that is indisting... | {
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1812.08226 | 2904013300 | In this paper, we investigate the possibility of applying plan transformations to general manipulation plans in order to specialize them to the specific situation at hand. We present a framework for optimizing execution and achieving higher performance by autonomously transforming robot's behavior at runtime. We show t... | From the area of automatic program transformations, Sussman's HACKER @cite_0 is a system which can change its programs when encountering a bug, and the knowledge from discovering the bug can be generalized and stored for future use. Transformations in HACKER result in programs with equivalent semantics. In robotics app... | {
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1812.08226 | 2904013300 | In this paper, we investigate the possibility of applying plan transformations to general manipulation plans in order to specialize them to the specific situation at hand. We present a framework for optimizing execution and achieving higher performance by autonomously transforming robot's behavior at runtime. We show t... | In multi-robot systems, Bothelho and Alami @cite_4 present an approach for an architecture, where autonomous robots can cooperatively enhance their execution performance by allowing them to detect and recover from failures, focusing on resource conflicts between the robots. implemented a distributed architecture @cite_... | {
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"Program transformation is used in a wide range of applications including compiler construction, optimization, program synthesis, refactoring, software renovation, and reverse engineering. Complex pr... |
1812.08352 | 2904407897 | In this paper, we introduce a new task - interactive image editing via conversational language, where users can guide an agent to edit images via multi-turn natural language dialogue. In each dialogue turn, the agent takes a source image and a natural language description from the user as the input and generates a new ... | Language-based image editing @cite_24 @cite_3 is a task designed for minimizing labor work while helping users create visual data. Specifically, systems that can perform automatic image editing should be able to understand which part of the image that the user is referring to. This is a very challenging task, which req... | {
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"We investigate the problem of Language-Based Image Editing (LBIE) in this work. Given a source image and a natural language description, we ... |
1812.08352 | 2904407897 | In this paper, we introduce a new task - interactive image editing via conversational language, where users can guide an agent to edit images via multi-turn natural language dialogue. In each dialogue turn, the agent takes a source image and a natural language description from the user as the input and generates a new ... | Since the introduction of GANs @cite_11 , there has been a surge of interest in image generation tasks. In the conditional GAN space, there have been some studies on generating images from images @cite_30 , captions @cite_32 attributes @cite_20 , and object-patch @cite_7 . There were also studies on how to parameterize... | {
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1812.08352 | 2904407897 | In this paper, we introduce a new task - interactive image editing via conversational language, where users can guide an agent to edit images via multi-turn natural language dialogue. In each dialogue turn, the agent takes a source image and a natural language description from the user as the input and generates a new ... | AttnGAN @cite_6 proposed by Xu embedded attention mechanism into the generator to focus on fine-grained word level information. Chen @cite_24 presented a framework targeting image segmentation and colorization with a recurrent attentive model. The FashionGAN work @cite_4 generated new clothing on a person based on text... | {
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"We investigate the problem of Language-Based Image Editing (LBIE) in this work. Given a source image and a natural language descri... |
1812.08352 | 2904407897 | In this paper, we introduce a new task - interactive image editing via conversational language, where users can guide an agent to edit images via multi-turn natural language dialogue. In each dialogue turn, the agent takes a source image and a natural language description from the user as the input and generates a new ... | AI tasks that lie in the intersection between computer vision and natural language processing have drawn much attention in the research community, benefiting from the latest deep learning techniques and GANs. Such tasks include visual question-answering @cite_40 , visual-semantic embeddings @cite_17 , grounding phrases... | {
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"We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question ... |
1812.08352 | 2904407897 | In this paper, we introduce a new task - interactive image editing via conversational language, where users can guide an agent to edit images via multi-turn natural language dialogue. In each dialogue turn, the agent takes a source image and a natural language description from the user as the input and generates a new ... | Most approaches have focused on end-to-end neural models based on the encoder-decoder architectures and sequence-to-sequence learning @cite_36 @cite_1 @cite_12 @cite_13 . Das @cite_16 proposed the task of visual dialogue, where the agent can answer questions about images in an interactive dialogue. De Vries @cite_15 in... | {
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1812.08125 | 2904944335 | Time-of-Flight (ToF) cameras require active illumination to obtain depth information thus the power of illumination directly affects the performance of ToF cameras. Traditional ToF imaging algorithms is very sensitive to illumination and the depth accuracy degenerates rapidly with the power of it. Therefore, the design... | Depth reconstruction based on ToF cameras. ToF cameras face a lot of challenging problems when extracting depth from raw phase-shifted measurements with respect to emitted modulated infrared signal. @cite_33 established a two-component, dual-frequency approach to resolving phase ambiguity, achieving significant improve... | {
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1812.08125 | 2904944335 | Time-of-Flight (ToF) cameras require active illumination to obtain depth information thus the power of illumination directly affects the performance of ToF cameras. Traditional ToF imaging algorithms is very sensitive to illumination and the depth accuracy degenerates rapidly with the power of it. Therefore, the design... | Image enhancement under low light. For conventional RGB cameras, photography in low light is challenging. Several techniques have been proposed to increase the SNR of the recovered image @cite_17 @cite_9 @cite_3 @cite_14 @cite_22 . Chen at el. @cite_21 established a pipeline by training a fully convolutional neural net... | {
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1812.08249 | 2903727631 | State-of-the-art methods for video action recognition commonly use an ensemble of two networks: the spatial stream, which takes RGB frames as input, and the temporal stream, which takes optical flow as input. In recent work, both of these streams consist of 3D Convolutional Neural Networks, which apply spatiotemporal f... | Many approaches leverage the strength of single-image (2D) CNNs by applying a CNN to each individual video frame and pooling the predictions across time @cite_34 @cite_17 @cite_13 . However, na " ve average pooling ignores the temporal dynamics of video. To capture temporal features, Two-Stream Networks introduce a sec... | {
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"We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the c... |
1812.08249 | 2903727631 | State-of-the-art methods for video action recognition commonly use an ensemble of two networks: the spatial stream, which takes RGB frames as input, and the temporal stream, which takes optical flow as input. In recent work, both of these streams consist of 3D Convolutional Neural Networks, which apply spatiotemporal f... | Many approaches have been proposed to incorporate motion features into 3D CNNs without the use of optical flow inputs. Motion Feature Networks, Optical Flow-Guided Features, and Representation Flow all accomplish this by introducing modules into the network architecture which explicitly compute motion representations @... | {
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1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | Gradient-based visualization @cite_19 @cite_45 @cite_37 estimates the input image that maximizes the activation score of a neural unit. Dosovitskiy @cite_55 proposed up-convolutional nets to invert feature maps of conv-layers to images. Unlike gradient-based methods, up-convolutional nets cannot mathematically ensure t... | {
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1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | Zhou @cite_7 proposed a method to accurately compute the image-resolution receptive field of neural activations in a feature map. Theoretically, the actual receptive field of a neural activation is smaller than that computed using the filter size. The accurate estimation of the receptive field is crucial to understand ... | {
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"With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art i... |
1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | @cite_51 explored semantic meanings of convolutional filters. @cite_3 evaluated the transferability of filters in intermediate conv-layers. @cite_42 @cite_67 computed feature distributions of different categories in the CNN feature space. Methods of @cite_47 @cite_38 propagated gradients of feature maps the CNN loss ba... | {
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1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | Network-attack methods @cite_21 @cite_52 @cite_51 diagnosed network representations by computing adversarial samples for a CNN. In particular, influence functions @cite_52 were proposed to compute adversarial samples, provide plausible ways to create training samples to attack the learning of CNNs, fix the training set... | {
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"Predictive models deployed in the real world may assign incorrect labels to instances with high confidence. Such errors or unknown unknown... |
1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | Zhang @cite_43 developed a method to examine representations of conv-layers and automatically discover potential, biased representations of a CNN due to the dataset bias. Furthermore, @cite_59 @cite_6 @cite_50 mined the local, bottom-up, and top-down information components in a model for prediction. | {
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1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | Hu @cite_58 designed logic rules for network outputs, and used these rules to regularize neural networks and learn meaningful representations. However, this study has not obtained semantic representations in intermediate layers. Some studies extracted neural units with certain semantics from CNNs for different applicat... | {
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1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | | | Many methods have been developed to learn object models in an unsupervised or weakly supervised manner. Methods of @cite_56 @cite_25 @cite_2 @cite_40 learned with image-level annotations without labeling object bounding boxes. @cite_0 @cite_41 did not require any annotations during the learning process. @cite_32 co... | {
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1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | Transferring hidden patterns in the CNN to other tasks is important for neural networks. Typical research includes end-to-end fine-tuning and transferring CNN representations between different categories @cite_3 @cite_48 or datasets @cite_27 . In contrast, we believe that a good explanation and transparent representati... | {
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1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | '' in un- weakly-supervised learning: | | Generally speaking, in the scenario of un- weakly-supervised learning, it is usually more difficult to model object parts than to represent entire objects. For example, object discovery @cite_69 @cite_40 @cite_62 and co-segmentation @cite_61 only require image-level labels with... | {
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"We present an algorithm for Interactive Co-segmentation of a foreground object from a group of related images. While previous works in co-... |
1812.07996 | 2904935312 | In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templ... | There are two key points to differentiate our study from conventional part-detection approaches. First, most detection methods deal with classification problems, but inspired by graph mining @cite_2 @cite_11 @cite_13 , we mainly focus on a mining problem. we aim to discover meaningful latent patterns to clarify CNN rep... | {
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1812.07989 | 2951745098 | Learning fine-grained details is a key issue in image aesthetic assessment. Most of the previous methods extract the fine-grained details via random cropping strategy, which may undermine the integrity of semantic information. Extensive studies show that humans perceive fine-grained details with a mixture of foveal vis... | There is a vast literature on the problem of designing effective features for aesthetic assessment, starting with the seminal work of @cite_10 and leading to recent works of @cite_21 @cite_14 @cite_4 . These features are based on the person's aesthetic perception and photographic rules. For example, Datta @cite_10 extr... | {
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1812.07989 | 2951745098 | Learning fine-grained details is a key issue in image aesthetic assessment. Most of the previous methods extract the fine-grained details via random cropping strategy, which may undermine the integrity of semantic information. Extensive studies show that humans perceive fine-grained details with a mixture of foveal vis... | Early attempts in image aesthetic assessment cast this problem as a classification problem, such as @cite_42 @cite_24 @cite_37 @cite_41 @cite_20 . They classified the images into high or low aesthetic quality based on the threshold of the weighted mean scores of human rating. Other research such as @cite_2 @cite_8 used... | {
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1812.07754 | 2905006172 | Voice-enabled commercial products are ubiquitous, typically enabled by lightweight on-device keyword spotting (KWS) and full automatic speech recognition (ASR) in the cloud. ASR systems require significant computational resources in training and for inference, not to mention copious amounts of annotated speech data. KW... | The typical approach to voice query recognition is to develop a full automatic speech recognition (ASR) system @cite_16 . Open-source toolkits like Kaldi @cite_4 provide ASR models to researchers; however, state-of-the-art commercial systems frequently require thousands of hours of training data @cite_6 and dozens of g... | {
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1812.07869 | 2904086477 | Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global... | most feature-based methods work by detecting feature points and matching them between consecutive frames. To improve pose accuracy, it minimizes the projective geometry errors between 3D feature points of the scene and their projection on the image plane, e.g., PTAM @cite_4 is a classical vSLAM system. However, it may ... | {
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1812.07869 | 2904086477 | Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global... | in contrast, direct methods estimate the camera motion by minimizing the photometric error over all pixels across consecutive images. Engel at al. @cite_9 developed LSD-SLAM, which is one of the most successful direct approaches. Direct methods do not provide better tolerance towards changing lighting conditions and of... | {
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1812.07869 | 2904086477 | Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global... | Learning-based relocalization systems are designed to learn from recognition to relocalization with very large scale classification datasets. For example, proposed PoseNet @cite_16 , which was the first successful end-to-end pre-trained deep CNNs approach for 6-DoF pose regression. In addition, @cite_7 introduced deep ... | {
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1812.07869 | 2904086477 | Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global... | learning-based visual odometry systems are employed to learn the incremental change in position from images. LS-VO @cite_5 is a CNNs architecture proposed to learn the latent space representation of the input Optical Flow field with the motion estimate task. SfM-Net @cite_22 is a self-supervised geometry-aware CNNs for... | {
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1812.07869 | 2904086477 | Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global... | More recently, learning-based global and relative networks are designed for 6-DoF global pose regression and odometry estimation from consecutive monocular images. VLocNet @cite_19 was a fusion architecture incorporates a global pose regression sub-networks and a Siamese-type relative pose estimation sub-networks. It t... | {
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1812.07762 | 2905141971 | Deep learning has improved many computer vision tasks by utilizing data-driven features instead of using hand-crafted features. However, geometric transformations of input images often degrade the performance of deep learning based methods. In particular, rotation-invariant features are important in computer vision tas... | . In general, max pooling layers in convolutional neural networks (CNN) are required to alleviate the issue of spatial variance in CNN. Assuming that spatial invariance is important for image classification, Jaderberg proposed spatial transformer network (STN), a method of image (or feature) transformation by learning ... | {
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1812.07762 | 2905141971 | Deep learning has improved many computer vision tasks by utilizing data-driven features instead of using hand-crafted features. However, geometric transformations of input images often degrade the performance of deep learning based methods. In particular, rotation-invariant features are important in computer vision tas... | Esteves proposed a rotation-invariant network by replacing the grid generation part in STN with a polar transform @cite_15 . They transformed the input feature map (or image) into the polar coordinate with the origin that was determined by the center of mass. They showed that polar coordinate allows to predict paramete... | {
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1812.07760 | 2951129992 | A safe and robust on-road navigation system is a crucial component of achieving fully automated vehicles. NVIDIA recently proposed an End-to-End algorithm that can directly learn steering commands from raw pixels of a front camera by using one convolutional neural network. In this paper, we leverage auxiliary informati... | Deep neural networks have been proven to be very successful in many fields. Recently a lot of work focuses on applying deep networks to learn driving policies from human demonstrations. One of the earliest attempts originates from ALVINN @cite_16 , which used a neural network to directly map front-view camera images to... | {
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1812.07760 | 2951129992 | A safe and robust on-road navigation system is a crucial component of achieving fully automated vehicles. NVIDIA recently proposed an End-to-End algorithm that can directly learn steering commands from raw pixels of a front camera by using one convolutional neural network. In this paper, we leverage auxiliary informati... | One way to overcome these problems is to train on a larger dataset. @cite_2 scaled this effort to a larger crowd-sourced dataset and proposed the FCN-LSTM architecture to derive a generic driving model. Another way is to set intermediate goals for the self-driving problem. @cite_3 and Al- @cite_20 map images to a numbe... | {
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1812.07760 | 2951129992 | A safe and robust on-road navigation system is a crucial component of achieving fully automated vehicles. NVIDIA recently proposed an End-to-End algorithm that can directly learn steering commands from raw pixels of a front camera by using one convolutional neural network. In this paper, we leverage auxiliary informati... | In this paper, we decide to improve learning driving policies by adding auxiliary tasks. The idea of using auxiliary tasks to aid the nominal task is not unprecedented. @cite_22 trained a reinforcement learning algorithm with unsupervised auxiliary tasks. By forecasting pixel changes and predicting rewards, the reinfor... | {
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1812.07807 | 2951825115 | Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very dee... | Our work is inspired by the deep transition RNN @cite_14 , which is applied on language modeling task. barone2017deep fist apply this kind of architecture on NMT, while there is still a large margin between this transition model and the state-of-the-art NMT models. Different from these works, we extremely enhance the d... | {
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1812.07807 | 2951825115 | Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very dee... | Our work is also inspired by deep stacked RNN models for NMT @cite_5 @cite_4 @cite_12 . ZhouCWLX16 propose fast-forward connections to address the notorious problem of vanishing exploding gradients for deep stacked RNMT. wangEtAl2017 propose the Linear Associative Unit (LAU) to reduce the gradient path inside the recur... | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | Similarly, let @math be spanned by vectors @math . Then @math can be described by the adjoint (analysis) operator [ S^* x S^* x = c, c[n]= x ,s_n , n , , x ] since by definition of adjoint operator @cite_0 [ S a, x = a, S^* x for all x , , a ( ). ] Note that the nullspace of @math is the orthogonal complement of @math ... | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | Under the frame assumption of @math , @math can be represented in terms of analysis and synthesis operators as @cite_17 where " @math " denotes the Moore-Penrose pseudoinverse. | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | According to @cite_17 , the orthogonal projection @math is subject to a fundamental limitation on the GSRP: Unless the reconstruction subspace is a subset of the sampling subspace, i.e., there exists no correction filter @math that renders the GSRP @math to be the orthogonal projection @math . | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | Acknowledging the optimality as well as the limitation of the orthogonal projection, we now introduce the difference between the GSRP @math and @math , which is, in the spirit of @cite_17 , referred to as the regret-error system: And the regret-error signal is given as [ R x = P_ x-x_r = ( P_ -W Q S^* )x. ] | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | Assume that the following direct-sum condition holds Then, the correction filter provides an error-free reconstruction for input signals in @math @cite_5 . | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | The absolute error for each input can be derived as follows: [ |E_ x |^2 = |P_ ^ ,x |^2 + |P_ P_ ^ ,x |^2, x . ] And the regret-error is [ | R_ x | = |P_ P_ ^ ,x |, x . ] From @cite_17 , the absolute error can be bounded in terms of the subspace angles as The regret-error is shown in to be bounded as | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | Recall that, filter @math is the minimizer of the reconstruction error for input @math , since it is the solution to the following optimization problem @cite_17 : where with @math representing the given sample sequence of input signal @math , and scalar @math being used as a bound of @math so that the objective functio... | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | Introduced in @cite_17 , the minimax regret sampling alleviates the drawback of large error associated with the consistent and subspace samplings. This is achieved by minimizing the maximum regret-error rather than the absolute error. | {
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1812.07776 | 2905503814 | This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes the reconstruction error for inputs in a subspace, subject to a constraint on the maximum regret-error for any other signal in the entire sig... | Consider the optimization problem: where Solution to ) is found to be Consequently, the GSRP becomes the product of two orthogonal projections Hence, the regret-error system is And the error system is Moreover, the regret-error is shown in @cite_17 to be bounded as Clearly, And since [ |E_ x |^2 ( 1+ ^2( , ) ) |P_ ^ x ... | {
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1812.07738 | 2904012376 | We study the risk performance of distributed learning for the regularization empirical risk minimization with fast convergence rate, substantially improving the error analysis of the existing divide-and-conquer based distributed learning. An interesting theoretical finding is that the larger the diversity of each local... | In this subsection, we will compare our bound with the related work @cite_9 @cite_6 @cite_15 . Under the smooth, strongly convex and other some assumptions, a distributed risk bound is given in @cite_9 : Under some eigenfunction assumption, the error analysis for distributed regularized least squares were established i... | {
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1812.07627 | 2905076505 | The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an altern... | Recently there have been many approaches using either cosine- or Gaussian-based loss functions. Most of these are used explicitly for the domain of image classification, where the problem of needing discriminative features is only understood as necessary for image problems, particularly facial recognition @cite_19 @cit... | {
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1812.07627 | 2905076505 | The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an altern... | In natural language processing, cosine similarity-based losses have only begun to be explored for the purpose of constructing more meaningful representations. In one case for the purpose of linearly constructing antonymous word embeddings @cite_26 , and also in a deep transfer learning task of building clusterable even... | {
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1812.07627 | 2905076505 | The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an altern... | Recent work @cite_20 has modelled classes with a set of Gaussians with the motivation of creating a well-structured latent space, using neighborhood-based sampling to maintain the centers of these Gaussians. However, this requires substantial architectural modifications to neural models, requiring frequent pauses durin... | {
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1812.07534 | 2903863706 | In this article, we investigate the impact of information on networked control systems, and illustrate how to quantify a fundamental property of stochastic processes that can enrich our understanding about such systems. To that end, we develop a theoretical framework for the joint design of an event trigger and a contr... | A special class of event-triggered estimation and event-triggered control is sensor scheduling in which open-loop triggering policies are employed. Sensor scheduling can be traced back to the 1970s. However, recently Trimpe and D'Andrea @cite_25 and Leong @cite_8 adopted sensor scheduling for networked control systems,... | {
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1812.07712 | 2905500782 | Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a given video clip. In this paper, a novel unsupervised video object segmentation approach via distra... | Given the manual foreground background annotations for the first frame in a video clip, semi-supervised VOS methods segment the foreground object along the remaining frames. Deep learning based methods have achieved excellent performance @cite_9 @cite_60 @cite_58 @cite_14 @cite_30 @cite_4 , and static image segmentatio... | {
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1812.07712 | 2905500782 | Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a given video clip. In this paper, a novel unsupervised video object segmentation approach via distra... | Unsupervised VOS algorithms @cite_19 @cite_40 @cite_57 @cite_34 @cite_8 @cite_32 @cite_62 @cite_39 @cite_37 attempt to extract the primary object segmentation with no manual annotations. Several unsupervised VOS algorithms @cite_31 @cite_21 cluster the boundary pixels hierarchically to generate mid-level video segmenta... | {
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1812.07712 | 2905500782 | Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a given video clip. In this paper, a novel unsupervised video object segmentation approach via distra... | Hard negative mining has also been exploited in deep learning models to improve the performance. OHEM @cite_45 trains region-based object detectors using automatically selected hard examples, and yields significant boosts in detection performance on both PASCAL @cite_6 and MS COCO @cite_29 datasets. Focal loss @cite_52... | {
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1812.07264 | 2947759080 | Abstract Cloud services and other shared third-party infrastructures allow individual content providers to easily scale their services based on current resource demands. In this paper, we consider an individual content provider that wants to minimize its delivery costs under the assumptions that the storage and bandwid... | Most existing caching works focus on replacement policies @cite_21 @cite_18 . However, recently it has been shown that the cache insertion policies play a very important factor in reducing the total delivery costs @cite_22 @cite_1 . Motivated by these works, this paper focuses on the delivery cost differences between d... | {
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1812.07264 | 2947759080 | Abstract Cloud services and other shared third-party infrastructures allow individual content providers to easily scale their services based on current resource demands. In this paper, we consider an individual content provider that wants to minimize its delivery costs under the assumptions that the storage and bandwid... | Few papers (regardless of replacement policy) have modeled discriminatory selective cache insertion policies such as . This class of policies is motivated by the risk of cache pollution due to ephemeral content popularity and the long tail of one-timers (one-hit wonders) observed in edge networks @cite_12 @cite_15 @cit... | {
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1812.07264 | 2947759080 | Abstract Cloud services and other shared third-party infrastructures allow individual content providers to easily scale their services based on current resource demands. In this paper, we consider an individual content provider that wants to minimize its delivery costs under the assumptions that the storage and bandwid... | Finally, it is important to note that TTL-based replacement eviction policies @cite_7 @cite_2 (and variations thereof @cite_4 ) (considered in this paper) have have been found useful for approximating the performance of capacity-driven replacement policies such as LRU @cite_26 @cite_3 @cite_28 @cite_14 @cite_24 . For a... | {
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1812.07264 | 2947759080 | Abstract Cloud services and other shared third-party infrastructures allow individual content providers to easily scale their services based on current resource demands. In this paper, we consider an individual content provider that wants to minimize its delivery costs under the assumptions that the storage and bandwid... | As we show here, these type of elasticity elasticity assumptions can also be a powerful toolbox for deriving tight worst-case bounds and exact average-case cost ratios of different policies. Furthermore, as argued in the paper, as discussed in Section 9.1, since both storage costs and bandwidth costs are proportional t... | {
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1812.07169 | 2904580001 | This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically. The analysis of the specific rationale of each prediction made by the CNN presents a key issue of understanding neural networks, but it is also of significant practical values in certai... | Distilling knowledge from a black-box model into an explainable model is an emerging direction in recent years. In contrast, we pursue the explicitly quantitative explanation for each CNN prediction. @cite_31 learned an explainable additive model, and @cite_30 distilled knowledge of a network into an additive model. @c... | {
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1812.07221 | 2905053447 | To continuously generate trajectories for serial manipulators with high dimensional degrees of freedom (DOF) in the dynamic environment, a real-time optimal trajectory generation method based on machine learning aiming at high dimensional inputs is presented in this paper. First, a learning optimization (LO) framework ... | The traditional point-to-point trajectory planning is started from interpolation-based methods, such as polynomial interpolation @cite_9 @cite_3 and B-spline interpolation @cite_12 @cite_15 . In general, pure interpolation-based methods are able to accomplish required tasks, but difficult to achieve the optimal perform... | {
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1812.07221 | 2905053447 | To continuously generate trajectories for serial manipulators with high dimensional degrees of freedom (DOF) in the dynamic environment, a real-time optimal trajectory generation method based on machine learning aiming at high dimensional inputs is presented in this paper. First, a learning optimization (LO) framework ... | The non-linear optimization is likely to get stuck into local minimum and generally solved with multiple initial guesses to obtain the global minimum, which is significant costly and hard to operate in real-time. Therefore, quickly seeking for the global minimum is still challenging. A promising idea is learning from f... | {
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1812.07221 | 2905053447 | To continuously generate trajectories for serial manipulators with high dimensional degrees of freedom (DOF) in the dynamic environment, a real-time optimal trajectory generation method based on machine learning aiming at high dimensional inputs is presented in this paper. First, a learning optimization (LO) framework ... | The database for learning can be obtained by either recording the former data, or artificially generating. @cite_17 evenly chose variables in motion range to generate samples and drew a comparison of databases with different sizes. Hauser @cite_11 uniformly sampled an axis-aligned range of variables to generate a datab... | {
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1812.07439 | 2905463339 | Probabilistic programming is a programming paradigm for expressing flexible probabilistic models. Implementations of probabilistic programming languages employ a variety of inference algorithms, where sequential Monte Carlo methods are commonly used. A problem with current state-of-the-art implementations using sequent... | Naturally, the work most closely related to ours can be found in papers on universal probabilistic programming languages using smc , such as WebPPL @cite_23 , Anglican @cite_19 , and Birch @cite_7 . Both WebPPL and Anglican are higher-order, functional ppl , while Birch is an imperative, object-oriented ppl . Anglican ... | {
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1812.07439 | 2905463339 | Probabilistic programming is a programming paradigm for expressing flexible probabilistic models. Implementations of probabilistic programming languages employ a variety of inference algorithms, where sequential Monte Carlo methods are commonly used. A problem with current state-of-the-art implementations using sequent... | There also exists more theoretical work on smc for probabilistic programming. One example is a recent denotational validation of smc in probabilistic programming given by @cite_11 . This work also includes a denotational validation of , another common inference algorithm for ppl . Trace mcmc has also been proven correc... | {
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1812.07170 | 2905288168 | Bug fixing is generally a manually-intensive task. However, recent work has proposed the idea of automated program repair, which aims to repair (at least a subset of) bugs in different ways such as code mutation, etc. Following in the same line of work as automated bug repair, in this paper we aim to leverage past fixe... | There are several studies on probabilistic machine learning models of source code for different applications using different techniques. Allamanis conducted a large survey on this topic @cite_4 . Table is originally presented in the survey of representative code models @cite_4 . From the original table, non-refereed pa... | {
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1812.07170 | 2905288168 | Bug fixing is generally a manually-intensive task. However, recent work has proposed the idea of automated program repair, which aims to repair (at least a subset of) bugs in different ways such as code mutation, etc. Following in the same line of work as automated bug repair, in this paper we aim to leverage past fixe... | From the data column, we see that several programing languages have been studied including Java, C, C #, JavaScript, Python, among others. Although most of studies collected data from code repositories, some used other data sources, for example, programs in TopCorder.com @cite_73 , Microsoft Excel help forums @cite_82 ... | {
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1812.07124 | 2952911521 | We propose a novel semi-supervised, Multi-Level Sequential Generative Adversarial Network (MLS-GAN) architecture for group activity recognition. In contrast to previous works which utilise manually annotated individual human action predictions, we allow the models to learn it's own internal representations to discover ... | Some early works @cite_6 @cite_5 @cite_29 @cite_28 on group activity recognition have addressed the group activity recognition task on surveillance and sports video datasets with probabilistic and discriminative models that utilise hand-crafted features. As these hand-crafted feature based methods always require featur... | {
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1812.07166 | 2904646808 | Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group... | Recent object detection models can be grouped into one of two types @cite_32 , two-stage approaches @cite_16 @cite_23 @cite_1 and one-stage methods @cite_2 @cite_20 . The former generates a series of candidate boxes as proposals by the algorithm, and then classifies the proposals by convolution neural network. The latt... | {
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1812.07166 | 2904646808 | Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group... | The inspiration of attention mechanism comes from the mechanism of human visual attention. Human vision is guided by attention which gives higher weights on objects than background. Recently, attention mechanism has been successfully applied in NLP @cite_10 @cite_7 @cite_11 @cite_28 @cite_27 as well as computer vision ... | {
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1812.07166 | 2904646808 | Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group... | Group convolution first appeared in AlexNet @cite_0 . To solve the problem of insufficient memory, AlexNet proposed that the group convolution approach could increase the diagonal correlation between filters and reduce the training parameters. Recently, many successful applications have proved the effectiveness of grou... | {
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1812.07134 | 2904473683 | This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean-squared-error (MSE) between the target and reconstructed images, we minimize a hybrid loss that consists of perceptual and adversarial loss... | Conventionally, HDR reconstruction has been performed by non-learning-based brightness enhancement through filtering or light-source detection. For example, bilateral filters applied to @math - @math -range three-dimensional grids work as brightness enhancement functions @cite_32 @cite_14 . However, non-learning-based ... | {
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1812.07134 | 2904473683 | This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean-squared-error (MSE) between the target and reconstructed images, we minimize a hybrid loss that consists of perceptual and adversarial loss... | An example of a multi-step methods is Deep Reverse Tone Mapping (DrTMO) @cite_10 , which generates multiple images with different exposures using an encoder-decoder network @cite_25 @cite_38 . To train the network, LDR images are simulated using various camera curves @cite_8 from an HDR image dataset and input. ChainHD... | {
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1812.07134 | 2904473683 | This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean-squared-error (MSE) between the target and reconstructed images, we minimize a hybrid loss that consists of perceptual and adversarial loss... | With the growing popularity of end-to-end learning, single-step networks that directly estimate the desired HDR images may be preferable to multi-step methods. HDR-CNN @cite_31 and Deep Reciprocating HDR @cite_29 share the same encoder-decoder structure that directly generates an HDR image from an LDR image. While the ... | {
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1812.07134 | 2904473683 | This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean-squared-error (MSE) between the target and reconstructed images, we minimize a hybrid loss that consists of perceptual and adversarial loss... | GANs have already been used for HDR image generation, by Lee al @cite_15 and Ning al @cite_12 . By introducing GAN, the restoration quality is further improved than the simple encoder-decoder networks . We found that GAN combined with reconstructive error still generates blur or unnatural artifacts. In this paper, by f... | {
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1812.07134 | 2904473683 | This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean-squared-error (MSE) between the target and reconstructed images, we minimize a hybrid loss that consists of perceptual and adversarial loss... | Apart from HDR reconstruction, we can see wider variety of deep-learning methods for image processing within LDR images, which are still useful as references. For example, convolutional GANs well-performed in superresolution @cite_30 , denoising @cite_28 , or inpaiting @cite_22 . Other than GANs, there are some promisi... | {
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1812.07145 | 2904366862 | Scene text recognition has received increased attention in the research community. Text in the wild often possesses irregular arrangements, typically including perspective text, curved text, oriented text. Most existing methods are hard to work well for irregular text, especially for severely distorted text. In this pa... | Scene text recognition has been widely researched and numerous methods are proposed in recent years. Traditional methods recognized scene text in a character-level manner, which first performed detection to generate multiple candidates of character locations, then applied a character classifier for recognition. Wang @c... | {
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1812.07145 | 2904366862 | Scene text recognition has received increased attention in the research community. Text in the wild often possesses irregular arrangements, typically including perspective text, curved text, oriented text. Most existing methods are hard to work well for irregular text, especially for severely distorted text. In this pa... | With the successful application of recurrent neural network (RNN) in sequence recognition, some researchers @cite_1 @cite_5 @cite_6 @cite_21 developed sequence-based methods and combined convolutional neural network (CNN) and RNN to encode the feature representations of word images. Shi @cite_1 and He @cite_5 both used... | {
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1812.07260 | 2905125557 | Interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design an efficient seed proposal method that acti... | Many well-known interactive image segmentation algorithms are in this category, , @cite_9 @cite_31 @cite_40 @cite_36 @cite_24 @cite_18 @cite_1 @cite_19 @cite_26 @cite_20 @cite_34 @cite_39 @cite_45 @cite_22 @cite_43 @cite_11 , in which the user directly specifies the location of each label via seeds scribbles @cite_9 @c... | {
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1812.07260 | 2905125557 | Interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design an efficient seed proposal method that acti... | Another line is the indirect interactive image segmentation @cite_17 @cite_41 @cite_3 @cite_13 @cite_5 , in which the algorithms usually recommend several uncertain regions to the user, and then the segmentation algorithms adopt the user-selected regions for updating the segmentation results. Batra @cite_17 propose a c... | {
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1812.07260 | 2905125557 | Interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design an efficient seed proposal method that acti... | The purpose of object proposal generation @cite_30 @cite_23 @cite_25 @cite_16 @cite_6 @cite_35 is to provide a relatively small set of bounding boxes or segments covering probable object locations in an image, so that an object detector does not have to examine exhaustively all possible locations in a sliding window ma... | {
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1812.07096 | 2898925204 | Abstract Wireless communication environments comprise passive objects that cause performance degradation and eavesdropping concerns due to anomalous scattering. This paper proposes a new paradigm, where scattering becomes software-defined and, subsequently, optimizable across wide frequency ranges. Through the proposed... | Phased array antennas have been used to actively and potentially adaptively alter the probabilistic behavior of a channel. Array panels hung from walls have been shown to influence considerably the communication quality of wireless devices . Phased array antennas comprise several half- or quarter-wavelength antennas, c... | {
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1812.07096 | 2898925204 | Abstract Wireless communication environments comprise passive objects that cause performance degradation and eavesdropping concerns due to anomalous scattering. This paper proposes a new paradigm, where scattering becomes software-defined and, subsequently, optimizable across wide frequency ranges. Through the proposed... | Un-phased antenna deployments have also been proposed as a cheaper and simpler alternative. In this case, simple antennas are placed over planar objects at relatively large distances to avoid coupling effects. Control over the EM waves is exert only at the antenna positions, while most of the surface of the planar obje... | {
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1812.07107 | 2916461310 | This article defines encrypted gate, which is denoted by @math . We present a gate-teleportation-based two-party computation scheme for @math , where one party gives arbitrary quantum state @math as input and obtains the encrypted @math -computing result @math , and the other party obtains the random bits @math . Based... | The QHE scheme EPR proposed by Broadbent and Jeffery @cite_6 makes use of Bell state and quantum measurement. That scheme is constructed by the combination of QOTP and classical FHE, then it is computational secure. Moreover, it is proved to be @math -quasi-compact, where @math is the number of @math -gates in an evalu... | {
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"abstract": [
"Fully homomorphic encryption is an encryption method with the property that any computation on the plaintext can be performed by a party having access to the ciphertext only. Here, we formally define and give schemes for quantum ho... |
1812.07107 | 2916461310 | This article defines encrypted gate, which is denoted by @math . We present a gate-teleportation-based two-party computation scheme for @math , where one party gives arbitrary quantum state @math as input and obtains the encrypted @math -computing result @math , and the other party obtains the random bits @math . Based... | @cite_15 prove a no-go result: if interaction is not allowed, there does not exist QFHE scheme with perfect security. An enhanced no-go result has been proved independently by Newman and Shi @cite_18 and Lai and Chung @cite_27 : if interaction is not allowed, there does not exist ITS QFHE scheme. This article focuses o... | {
"cite_N": [
"@cite_27",
"@cite_15",
"@cite_18"
],
"mid": [
"",
"2079128711",
"2098611377"
],
"abstract": [
"",
"Homomorphic encryption is a form of encryption which allows computation to be carried out on the encrypted data without the need for decryption. The success of quan... |
1812.07060 | 2905308012 | Neural network pruning is an important step in design process of efficient neural networks for edge devices with limited computational power. Pruning is a form of knowledge transfer from the weights of the original network to a smaller target subnetwork. We propose a new method for compute-constrained structured channe... | One large branch of pruning methods stems from the basic scheme of Han et al. (2015) @cite_18 , we'll call them heuristic methods''. These methods repeatedly choose elements based on some scalar metric (salience), and remove them from the network. Each iteration of removal is followed by fine-tuning. Salience can be ba... | {
"cite_N": [
"@cite_18",
"@cite_14",
"@cite_22",
"@cite_7",
"@cite_21",
"@cite_1",
"@cite_3",
"@cite_6",
"@cite_27",
"@cite_2",
"@cite_5",
"@cite_25",
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"@cite_17"
],
"mid": [
"1845051632",
"992687842",
"",
"",
"",
"",
... |
1812.07060 | 2905308012 | Neural network pruning is an important step in design process of efficient neural networks for edge devices with limited computational power. Pruning is a form of knowledge transfer from the weights of the original network to a smaller target subnetwork. We propose a new method for compute-constrained structured channe... | Fisher pruning'' @cite_15 resembles these heuristic'' methods, but its salience is based on the method of Lagrange multipliers, which makes this method resource-aware and less heuristic. It removes a channel every pruning iteration, so its pruning speed is fixed and doesn't slow down. | {
"cite_N": [
"@cite_15"
],
"mid": [
"2783873922"
],
"abstract": [
"Predicting human fixations from images has recently seen large improvements by leveraging deep representations which were pretrained for object recognition. However, as we show in this paper, these networks are highly overparamete... |
1812.07060 | 2905308012 | Neural network pruning is an important step in design process of efficient neural networks for edge devices with limited computational power. Pruning is a form of knowledge transfer from the weights of the original network to a smaller target subnetwork. We propose a new method for compute-constrained structured channe... | The method from @cite_23 trains channel scaling factors to simulate channel granularity pruning, however the factors are not limited to @math range. The factors are updated with an SGD-like method called ISTA, that includes a sparsity-inducing @math regularization term resembling the Lagrangian term, which also makes t... | {
"cite_N": [
"@cite_23"
],
"mid": [
"2786054724"
],
"abstract": [
"Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions on resource-limited scenarios. A widely-used practice in relevant work assumes that a ... |
1812.07060 | 2905308012 | Neural network pruning is an important step in design process of efficient neural networks for edge devices with limited computational power. Pruning is a form of knowledge transfer from the weights of the original network to a smaller target subnetwork. We propose a new method for compute-constrained structured channe... | Structured Probabilistic Pruning @cite_19 trains probabilities of channel removal. Every pruning iteration the probabilities are updated with a heuristic rule based on the rank of the channel across all layers by @math metric of channel weights. This requires the user to define the desired number of channels in advance... | {
"cite_N": [
"@cite_19"
],
"mid": [
"2757143157"
],
"abstract": [
"Although deep Convolutional Neural Network (CNN) has shown better performance in various computer vision tasks, its application is restricted by a significant increase in storage and computation. Among CNN simplification technique... |
1812.07067 | 2903794034 | In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree st... | Facial expression recognition has been extensively studied as elaborated in the recent surveys @cite_2 @cite_7 . One of the major steps in facial expression recognition is to extract features that capture the appearance and geometry changes caused by facial behavior, from either static images or dynamic sequences. Thes... | {
"cite_N": [
"@cite_7",
"@cite_2"
],
"mid": [
"2737559518",
"1965947362"
],
"abstract": [
"As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has recently received significant attention. Over the past 30 years, extens... |
1812.07067 | 2903794034 | In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree st... | More recently, identity information is explicitly taken into consideration when learning the deep models. An identity-aware CNN @cite_15 developed an identity-sensitive contrastive loss to learn identity-related features. An Identity-Adaptive Generation (IA-gen) method @cite_20 was proposed to synthesize person-depende... | {
"cite_N": [
"@cite_15",
"@cite_42",
"@cite_20"
],
"mid": [
"2730601341",
"2798583514",
"2805080735"
],
"abstract": [
"Facial expression recognition suffers under realworldconditions, especially on unseen subjects due to highinter-subject variations. To alleviate variations introd... |
1812.07067 | 2903794034 | In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree st... | Apart from learning identity-free expression-related features, Multi-task Learning (MTL) has been employed @cite_22 to simultaneously perform various face-related tasks including detection, alignment, pose estimation, gender recognition, age estimation, smile detection, and face recognition using a single deep CNN. To ... | {
"cite_N": [
"@cite_22"
],
"mid": [
"2548780814"
],
"abstract": [
"We present a multi-purpose algorithm for simultaneousface detection, face alignment, pose estimation, genderrecognition, smile detection, age estimation and face recognitionusing a single deep convolutional neural network (CNN). T... |
1812.07067 | 2903794034 | In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree st... | Recently, clustering has been utilized to group deep features. A recurrent framework @cite_41 updates deep features and image clusters alternatively until the number of clusters reaches the predefined value. DeepCluster @cite_38 alternatively groups the features by k-means and uses the subsequent assignments as supervi... | {
"cite_N": [
"@cite_41",
"@cite_38",
"@cite_14"
],
"mid": [
"2337374958",
"",
"2799118171"
],
"abstract": [
"In this paper, we propose a recurrent framework for Joint Unsupervised LEarning (JULE) of deep representations and image clusters. In our framework, successive operations i... |
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