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3,000 | Gearbox Fault Detection through PSO Exact Wavelet Analysis and SVM
Classifier | cs.LG | Time-frequency methods for vibration-based gearbox faults detection have been
considered the most efficient method. Among these methods, continuous wavelet
transform (CWT) as one of the best time-frequency method has been used for both
stationary and transitory signals. Some deficiencies of CWT are problem of
overlappi... | computer science |
3,001 | Unsupervised Learning for Physical Interaction through Video Prediction | cs.LG | A core challenge for an agent learning to interact with the world is to
predict how its actions affect objects in its environment. Many existing
methods for learning the dynamics of physical interactions require labeled
object information. However, to scale real-world interaction learning to a
variety of scenes and obj... | computer science |
3,002 | SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks | cs.LG | We introduce SE3-Nets, which are deep neural networks designed to model and
learn rigid body motion from raw point cloud data. Based only on sequences of
depth images along with action vectors and point wise data associations,
SE3-Nets learn to segment effected object parts and predict their motion
resulting from the a... | computer science |
3,003 | Piecewise convexity of artificial neural networks | cs.LG | Although artificial neural networks have shown great promise in applications
including computer vision and speech recognition, there remains considerable
practical and theoretical difficulty in optimizing their parameters. The
seemingly unreasonable success of gradient descent methods in minimizing these
non-convex fun... | computer science |
3,004 | What makes ImageNet good for transfer learning? | cs.CV | The tremendous success of ImageNet-trained deep features on a wide range of
transfer tasks begs the question: what are the properties of the ImageNet
dataset that are critical for learning good, general-purpose features? This
work provides an empirical investigation of various facets of this question: Is
more pre-train... | computer science |
3,005 | UberNet: Training a `Universal' Convolutional Neural Network for Low-,
Mid-, and High-Level Vision using Diverse Datasets and Limited Memory | cs.CV | In this work we introduce a convolutional neural network (CNN) that jointly
handles low-, mid-, and high-level vision tasks in a unified architecture that
is trained end-to-end. Such a universal network can act like a `swiss knife'
for vision tasks; we call this architecture an UberNet to indicate its
overarching natur... | computer science |
3,006 | Deep Tracking on the Move: Learning to Track the World from a Moving
Vehicle using Recurrent Neural Networks | cs.CV | This paper presents an end-to-end approach for tracking static and dynamic
objects for an autonomous vehicle driving through crowded urban environments.
Unlike traditional approaches to tracking, this method is learned end-to-end,
and is able to directly predict a full unoccluded occupancy grid map from raw
laser input... | computer science |
3,007 | Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for
Urban Autonomy | cs.RO | We present a weakly-supervised approach to segmenting proposed drivable paths
in images with the goal of autonomous driving in complex urban environments.
Using recorded routes from a data collection vehicle, our proposed method
generates vast quantities of labelled images containing proposed paths and
obstacles withou... | computer science |
3,008 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
Localization | cs.CV | We propose a technique for producing "visual explanations" for decisions from
a large class of CNN-based models, making them more transparent. Our approach -
Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of
any target concept, flowing into the final convolutional layer to produce a
coarse lo... | computer science |
3,009 | Deep Fruit Detection in Orchards | cs.RO | An accurate and reliable image based fruit detection system is critical for
supporting higher level agriculture tasks such as yield mapping and robotic
harvesting. This paper presents the use of a state-of-the-art object detection
framework, Faster R-CNN, in the context of fruit detection in orchards,
including mangoes... | computer science |
3,010 | Bit-pragmatic Deep Neural Network Computing | cs.LG | We quantify a source of ineffectual computations when processing the
multiplications of the convolutional layers in Deep Neural Networks (DNNs) and
propose Pragmatic (PRA), an architecture that exploits it improving performance
and energy efficiency. The source of these ineffectual computations is best
understood in th... | computer science |
3,011 | Learning to Act by Predicting the Future | cs.LG | We present an approach to sensorimotor control in immersive environments. Our
approach utilizes a high-dimensional sensory stream and a lower-dimensional
measurement stream. The cotemporal structure of these streams provides a rich
supervisory signal, which enables training a sensorimotor control model by
interacting w... | computer science |
3,012 | Learning to Navigate in Complex Environments | cs.AI | Learning to navigate in complex environments with dynamic elements is an
important milestone in developing AI agents. In this work we formulate the
navigation question as a reinforcement learning problem and show that data
efficiency and task performance can be dramatically improved by relying on
additional auxiliary t... | computer science |
3,013 | DeepSetNet: Predicting Sets with Deep Neural Networks | cs.CV | This paper addresses the task of set prediction using deep learning. This is
important because the output of many computer vision tasks, including image
tagging and object detection, are naturally expressed as sets of entities
rather than vectors. As opposed to a vector, the size of a set is not fixed in
advance, and i... | computer science |
3,014 | Measuring and modeling the perception of natural and unconstrained gaze
in humans and machines | cs.AI | Humans are remarkably adept at interpreting the gaze direction of other
individuals in their surroundings. This skill is at the core of the ability to
engage in joint visual attention, which is essential for establishing social
interactions. How accurate are humans in determining the gaze direction of
others in lifelik... | computer science |
3,015 | Understanding the Effective Receptive Field in Deep Convolutional Neural
Networks | cs.CV | We study characteristics of receptive fields of units in deep convolutional
networks. The receptive field size is a crucial issue in many visual tasks, as
the output must respond to large enough areas in the image to capture
information about large objects. We introduce the notion of an effective
receptive field, and s... | computer science |
3,016 | Deep Learning with Low Precision by Half-wave Gaussian Quantization | cs.CV | The problem of quantizing the activations of a deep neural network is
considered. An examination of the popular binary quantization approach shows
that this consists of approximating a classical non-linearity, the hyperbolic
tangent, by two functions: a piecewise constant sign function, which is used in
feedforward net... | computer science |
3,017 | Cognitive Mapping and Planning for Visual Navigation | cs.CV | We introduce a neural architecture for navigation in novel environments. Our
proposed architecture learns to map from first-person views and plans a
sequence of actions towards goals in the environment. The Cognitive Mapper and
Planner (CMP) is based on two key ideas: a) a unified joint architecture for
mapping and pla... | computer science |
3,018 | Regularizing Face Verification Nets For Pain Intensity Regression | cs.CV | Limited labeled data are available for the research of estimating facial
expression intensities. For instance, the ability to train deep networks for
automated pain assessment is limited by small datasets with labels of
patient-reported pain intensities. Fortunately, fine-tuning from a
data-extensive pre-trained domain... | computer science |
3,019 | DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy | cs.CV | We introduce DeepNAT, a 3D Deep convolutional neural network for the
automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance
images. DeepNAT is an end-to-end learning-based approach to brain segmentation
that jointly learns an abstract feature representation and a multi-class
classification. We propose... | computer science |
3,020 | Learning Robot Activities from First-Person Human Videos Using
Convolutional Future Regression | cs.RO | We design a new approach that allows robot learning of new activities from
unlabeled human example videos. Given videos of humans executing the same
activity from a human's viewpoint (i.e., first-person videos), our objective is
to make the robot learn the temporal structure of the activity as its future
regression net... | computer science |
3,021 | Deep Variation-structured Reinforcement Learning for Visual Relationship
and Attribute Detection | cs.CV | Despite progress in visual perception tasks such as image classification and
detection, computers still struggle to understand the interdependency of
objects in the scene as a whole, e.g., relations between objects or their
attributes. Existing methods often ignore global context cues capturing the
interactions among d... | computer science |
3,022 | Interpretable Structure-Evolving LSTM | cs.CV | This paper develops a general framework for learning interpretable data
representation via Long Short-Term Memory (LSTM) recurrent neural networks over
hierarchal graph structures. Instead of learning LSTM models over the pre-fixed
structures, we propose to further learn the intermediate interpretable
multi-level graph... | computer science |
3,023 | Deep Value Networks Learn to Evaluate and Iteratively Refine Structured
Outputs | cs.LG | We approach structured output prediction by optimizing a deep value network
(DVN) to precisely estimate the task loss on different output configurations
for a given input. Once the model is trained, we perform inference by gradient
descent on the continuous relaxations of the output variables to find outputs
with promi... | computer science |
3,024 | Look into Person: Self-supervised Structure-sensitive Learning and A New
Benchmark for Human Parsing | cs.CV | Human parsing has recently attracted a lot of research interests due to its
huge application potentials. However existing datasets have limited number of
images and annotations, and lack the variety of human appearances and the
coverage of challenging cases in unconstrained environment. In this paper, we
introduce a ne... | computer science |
3,025 | Recurrent Topic-Transition GAN for Visual Paragraph Generation | cs.CV | A natural image usually conveys rich semantic content and can be viewed from
different angles. Existing image description methods are largely restricted by
small sets of biased visual paragraph annotations, and fail to cover rich
underlying semantics. In this paper, we investigate a semi-supervised paragraph
generative... | computer science |
3,026 | InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations | cs.LG | The goal of imitation learning is to mimic expert behavior without access to
an explicit reward signal. Expert demonstrations provided by humans, however,
often show significant variability due to latent factors that are typically not
explicitly modeled. In this paper, we propose a new algorithm that can infer
the late... | computer science |
3,027 | Perception Driven Texture Generation | cs.CV | This paper investigates a novel task of generating texture images from
perceptual descriptions. Previous work on texture generation focused on either
synthesis from examples or generation from procedural models. Generating
textures from perceptual attributes have not been well studied yet. Meanwhile,
perceptual attribu... | computer science |
3,028 | LabelBank: Revisiting Global Perspectives for Semantic Segmentation | cs.CV | Semantic segmentation requires a detailed labeling of image pixels by object
category. Information derived from local image patches is necessary to describe
the detailed shape of individual objects. However, this information is
ambiguous and can result in noisy labels. Global inference of image content can
instead capt... | computer science |
3,029 | Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation | cs.CV | Many modern computer vision and machine learning applications rely on solving
difficult optimization problems that involve non-differentiable objective
functions and constraints. The alternating direction method of multipliers
(ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a
generalization of ... | computer science |
3,030 | Semantically Consistent Regularization for Zero-Shot Recognition | cs.CV | The role of semantics in zero-shot learning is considered. The effectiveness
of previous approaches is analyzed according to the form of supervision
provided. While some learn semantics independently, others only supervise the
semantic subspace explained by training classes. Thus, the former is able to
constrain the wh... | computer science |
3,031 | Deep Face Deblurring | cs.CV | Blind deblurring consists a long studied task, however the outcomes of
generic methods are not effective in real world blurred images. Domain-specific
methods for deblurring targeted object categories, e.g. text or faces,
frequently outperform their generic counterparts, hence they are attracting an
increasing amount o... | computer science |
3,032 | Show, Adapt and Tell: Adversarial Training of Cross-domain Image
Captioner | cs.CV | Impressive image captioning results are achieved in domains with plenty of
training image and sentence pairs (e.g., MSCOCO). However, transferring to a
target domain with significant domain shifts but no paired training data
(referred to as cross-domain image captioning) remains largely unexplored. We
propose a novel a... | computer science |
3,033 | TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based
Classification Using Recurrent Neural Networks | cs.CV | Understanding and discovering knowledge from GPS (Global Positioning System)
traces of human activities is an essential topic in mobility-based urban
computing. We propose TrajectoryNet-a neural network architecture for
point-based trajectory classification to infer real world human transportation
modes from GPS traces... | computer science |
3,034 | CORe50: a New Dataset and Benchmark for Continuous Object Recognition | cs.CV | Continuous/Lifelong learning of high-dimensional data streams is a
challenging research problem. In fact, fully retraining models each time new
data become available is infeasible, due to computational and storage issues,
while na\"ive incremental strategies have been shown to suffer from
catastrophic forgetting. In th... | computer science |
3,035 | Learning to see people like people | cs.CV | Humans make complex inferences on faces, ranging from objective properties
(gender, ethnicity, expression, age, identity, etc) to subjective judgments
(facial attractiveness, trustworthiness, sociability, friendliness, etc). While
the objective aspects of face perception have been extensively studied,
relatively fewer ... | computer science |
3,036 | Recurrent computations for visual pattern completion | cs.AI | Making inferences from partial information constitutes a critical aspect of
cognition. During visual perception, pattern completion enables recognition of
poorly visible or occluded objects. We combined psychophysics, physiology and
computational models to test the hypothesis that pattern completion is
implemented by r... | computer science |
3,037 | Training a Fully Convolutional Neural Network to Route Integrated
Circuits | cs.CV | We present a deep, fully convolutional neural network that learns to route a
circuit layout net with appropriate choice of metal tracks and wire class
combinations. Inputs to the network are the encoded layouts containing spatial
location of pins to be routed. After 15 fully convolutional stages followed by
a score com... | computer science |
3,038 | Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images
And Text | cs.MM | Real world multimedia data is often composed of multiple modalities such as
an image or a video with associated text (e.g. captions, user comments, etc.)
and metadata. Such multimodal data packages are prone to manipulations, where a
subset of these modalities can be altered to misrepresent or repurpose data
packages, ... | computer science |
3,039 | CNN features are also great at unsupervised classification | cs.CV | This paper aims at providing insight on the transferability of deep CNN
features to unsupervised problems. We study the impact of different pretrained
CNN feature extractors on the problem of image set clustering for object
classification as well as fine-grained classification. We propose a rather
straightforward pipel... | computer science |
3,040 | Deformable Part-based Fully Convolutional Network for Object Detection | cs.CV | Existing region-based object detectors are limited to regions with fixed box
geometry to represent objects, even if those are highly non-rectangular. In
this paper we introduce DP-FCN, a deep model for object detection which
explicitly adapts to shapes of objects with deformable parts. Without
additional annotations, i... | computer science |
3,041 | Face Deidentification with Generative Deep Neural Networks | cs.CV | Face deidentification is an active topic amongst privacy and security
researchers. Early deidentification methods relying on image blurring or
pixelization were replaced in recent years with techniques based on formal
anonymity models that provide privacy guaranties and at the same time aim at
retaining certain charact... | computer science |
3,042 | Photographic Image Synthesis with Cascaded Refinement Networks | cs.CV | We present an approach to synthesizing photographic images conditioned on
semantic layouts. Given a semantic label map, our approach produces an image
with photographic appearance that conforms to the input layout. The approach
thus functions as a rendering engine that takes a two-dimensional semantic
specification of ... | computer science |
3,043 | Learning Efficient Convolutional Networks through Network Slimming | cs.CV | The deployment of deep convolutional neural networks (CNNs) in many real
world applications is largely hindered by their high computational cost. In
this paper, we propose a novel learning scheme for CNNs to simultaneously 1)
reduce the model size; 2) decrease the run-time memory footprint; and 3) lower
the number of c... | computer science |
3,044 | Generating Visual Representations for Zero-Shot Classification | cs.CV | This paper addresses the task of learning an image clas-sifier when some
categories are defined by semantic descriptions only (e.g. visual attributes)
while the others are defined by exemplar images as well. This task is often
referred to as the Zero-Shot classification task (ZSC). Most of the previous
methods rely on ... | computer science |
3,045 | 3D Object Reconstruction from a Single Depth View with Adversarial
Learning | cs.CV | In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the
complete 3D structure of a given object from a single arbitrary depth view
using generative adversarial networks. Unlike the existing work which typically
requires multiple views of the same object or class labels to recover the full
3D geomet... | computer science |
3,046 | Limiting the Reconstruction Capability of Generative Neural Network
using Negative Learning | cs.CV | Generative models are widely used for unsupervised learning with various
applications, including data compression and signal restoration. Training
methods for such systems focus on the generality of the network given limited
amount of training data. A less researched type of techniques concerns
generation of only a sin... | computer science |
3,047 | Fine-tuning deep CNN models on specific MS COCO categories | cs.CV | Fine-tuning of a deep convolutional neural network (CNN) is often desired.
This paper provides an overview of our publicly available py-faster-rcnn-ft
software library that can be used to fine-tune the VGG_CNN_M_1024 model on
custom subsets of the Microsoft Common Objects in Context (MS COCO) dataset.
For example, we i... | computer science |
3,048 | Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A
Deep Learning Approach | cs.CV | Emotion recognition from facial expressions is tremendously useful,
especially when coupled with smart devices and wireless multimedia
applications. However, the inadequate network bandwidth often limits the
spatial resolution of the transmitted video, which will heavily degrade the
recognition reliability. We develop ... | computer science |
3,049 | Art of singular vectors and universal adversarial perturbations | cs.CV | Vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has been
attracting a lot of attention in recent studies. It has been shown that for
many state of the art DNNs performing image classification there exist
universal adversarial perturbations --- image-agnostic perturbations mere
addition of which to n... | computer science |
3,050 | Denoising Autoencoders for Overgeneralization in Neural Networks | cs.AI | Despite the recent developments that allowed neural networks to achieve
impressive performance on a variety of applications, these models are
intrinsically affected by the problem of overgeneralization, due to their
partitioning of the full input space into the fixed set of target classes used
during training. Thus it ... | computer science |
3,051 | One-Shot Visual Imitation Learning via Meta-Learning | cs.LG | In order for a robot to be a generalist that can perform a wide range of
jobs, it must be able to acquire a wide variety of skills quickly and
efficiently in complex unstructured environments. High-capacity models such as
deep neural networks can enable a robot to represent complex skills, but
learning each skill from ... | computer science |
3,052 | Convolutional neural networks that teach microscopes how to image | cs.CV | Deep learning algorithms offer a powerful means to automatically analyze the
content of medical images. However, many biological samples of interest are
primarily transparent to visible light and contain features that are difficult
to resolve with a standard optical microscope. Here, we use a convolutional
neural netwo... | computer science |
3,053 | Using Simulation and Domain Adaptation to Improve Efficiency of Deep
Robotic Grasping | cs.LG | Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, m... | computer science |
3,054 | Dose Prediction with U-net: A Feasibility Study for Predicting Dose
Distributions from Contours using Deep Learning on Prostate IMRT Patients | cs.AI | With the advancement of treatment modalities in radiation therapy, outcomes
haves greatly improved, but at the cost of increased treatment plan complexity
and planning time. The accurate prediction of dose distributions would
alleviate this issue by guiding clinical plan optimization to save time and
maintain high qual... | computer science |
3,055 | Are we Done with Object Recognition? The iCub robot's Perspective | cs.RO | We report on an extensive study of the current benefits and limitations of
deep learning approaches to robot vision and introduce a novel dataset used for
our investigation. To avoid the biases in currently available datasets, we
consider a human-robot interaction setting to design a data-acquisition
protocol for visua... | computer science |
3,056 | Projection Based Weight Normalization for Deep Neural Networks | cs.LG | Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned
problem. We observe that the scaling-based weight space symmetry property in
rectified nonlinear network will cause this negative effect. Therefore, we
propose to constrain the incoming weights of each neuron to be unit-norm, which
is formula... | computer science |
3,057 | Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from
Simulation | cs.LG | Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB i... | computer science |
3,058 | Max-Margin Invariant Features from Transformed Unlabeled Data | cs.LG | The study of representations invariant to common transformations of the data
is important to learning. Most techniques have focused on local approximate
invariance implemented within expensive optimization frameworks lacking
explicit theoretical guarantees. In this paper, we study kernels that are
invariant to a unitar... | computer science |
3,059 | Acquiring Target Stacking Skills by Goal-Parameterized Deep
Reinforcement Learning | cs.RO | Understanding physical phenomena is a key component of human intelligence and
enables physical interaction with previously unseen environments. In this
paper, we study how an artificial agent can autonomously acquire this intuition
through interaction with the environment. We created a synthetic block stacking
environm... | computer science |
3,060 | Ensembles of Multiple Models and Architectures for Robust Brain Tumour
Segmentation | cs.CV | Deep learning approaches such as convolutional neural nets have consistently
outperformed previous methods on challenging tasks such as dense, semantic
segmentation. However, the various proposed networks perform differently, with
behaviour largely influenced by architectural choices and training settings.
This paper e... | computer science |
3,061 | CARLA: An Open Urban Driving Simulator | cs.LG | We introduce CARLA, an open-source simulator for autonomous driving research.
CARLA has been developed from the ground up to support development, training,
and validation of autonomous urban driving systems. In addition to open-source
code and protocols, CARLA provides open digital assets (urban layouts,
buildings, veh... | computer science |
3,062 | High-Order Attention Models for Visual Question Answering | cs.CV | The quest for algorithms that enable cognitive abilities is an important part
of machine learning. A common trait in many recently investigated
cognitive-like tasks is that they take into account different data modalities,
such as visual and textual input. In this paper we propose a novel and
generally applicable form ... | computer science |
3,063 | Machine Learning for the Geosciences: Challenges and Opportunities | cs.LG | Geosciences is a field of great societal relevance that requires solutions to
several urgent problems facing our humanity and the planet. As geosciences
enters the era of big data, machine learning (ML) -- that has been widely
successful in commercial domains -- offers immense potential to contribute to
problems in geo... | computer science |
3,064 | Spatio-Temporal Data Mining: A Survey of Problems and Methods | cs.LG | Large volumes of spatio-temporal data are increasingly collected and studied
in diverse domains including, climate science, social sciences, neuroscience,
epidemiology, transportation, mobile health, and Earth sciences.
Spatio-temporal data differs from relational data for which computational
approaches are developed i... | computer science |
3,065 | Loss Functions for Multiset Prediction | cs.LG | We study the problem of multiset prediction. The goal of multiset prediction
is to train a predictor that maps an input to a multiset consisting of multiple
items. Unlike existing problems in supervised learning, such as classification,
ranking and sequence generation, there is no known order among items in a
target mu... | computer science |
3,066 | CleanNet: Transfer Learning for Scalable Image Classifier Training with
Label Noise | cs.CV | In this paper, we study the problem of learning image classification models
with label noise. Existing approaches depending on human supervision are
generally not scalable as manually identifying correct or incorrect labels is
timeconsuming, whereas approaches not relying on human supervision are scalable
but less effe... | computer science |
3,067 | Relating Input Concepts to Convolutional Neural Network Decisions | cs.LG | Many current methods to interpret convolutional neural networks (CNNs) use
visualization techniques and words to highlight concepts of the input seemingly
relevant to a CNN's decision. The methods hypothesize that the recognition of
these concepts are instrumental in the decision a CNN reaches, but the nature
of this r... | computer science |
3,068 | FearNet: Brain-Inspired Model for Incremental Learning | cs.LG | Incremental class learning involves sequentially learning classes in bursts
of examples from the same class. This violates the assumptions that underlie
methods for training standard deep neural networks, and will cause them to
suffer from catastrophic forgetting. Arguably, the best method for incremental
class learnin... | computer science |
3,069 | Compatibility Family Learning for Item Recommendation and Generation | cs.LG | Compatibility between items, such as clothes and shoes, is a major factor
among customer's purchasing decisions. However, learning "compatibility" is
challenging due to (1) broader notions of compatibility than those of
similarity, (2) the asymmetric nature of compatibility, and (3) only a small
set of compatible and i... | computer science |
3,070 | Adversarial Examples that Fool Detectors | cs.CV | An adversarial example is an example that has been adjusted to produce a
wrong label when presented to a system at test time. To date, adversarial
example constructions have been demonstrated for classifiers, but not for
detectors. If adversarial examples that could fool a detector exist, they could
be used to (for exa... | computer science |
3,071 | Learning Nested Sparse Structures in Deep Neural Networks | cs.CV | Recently, there have been increasing demands to construct compact deep
architectures to remove unnecessary redundancy and to improve the inference
speed. While many recent works focus on reducing the redundancy by eliminating
unneeded weight parameters, it is not possible to apply a single deep
architecture for multipl... | computer science |
3,072 | RESIDE: A Benchmark for Single Image Dehazing | cs.CV | In this paper, we present a comprehensive study and evaluation of existing
single image dehazing algorithms, using a new large-scale benchmark consisting
of both synthetic and real-world hazy images, called REalistic Single Image
DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents,
and is divid... | computer science |
3,073 | Visual Explanations from Hadamard Product in Multimodal Deep Networks | cs.CV | The visual explanation of learned representation of models helps to
understand the fundamentals of learning. The attentional models of previous
works used to visualize the attended regions over an image or text using their
learned weights to confirm their intended mechanism. Kim et al. (2016) show
that the Hadamard pro... | computer science |
3,074 | Combination of Hyperband and Bayesian Optimization for Hyperparameter
Optimization in Deep Learning | cs.CV | Deep learning has achieved impressive results on many problems. However, it
requires high degree of expertise or a lot of experience to tune well the
hyperparameters, and such manual tuning process is likely to be biased.
Moreover, it is not practical to try out as many different hyperparameter
configurations in deep l... | computer science |
3,075 | Can Computers Create Art? | cs.AI | This essay discusses whether computers, using Artificial Intelligence (AI),
could create art. The first part concerns AI-based tools for assisting with art
making. The history of technologies that automated aspects of art is covered,
including photography and animation. In each case, we see initial fears and
denial of ... | computer science |
3,076 | Non-Parametric Transformation Networks | cs.CV | ConvNets, through their architecture, only enforce invariance to translation.
In this paper, we introduce a new class of deep convolutional architectures
called Non-Parametric Transformation Networks (NPTNs) which can learn
\textit{general} invariances and symmetries directly from data. NPTNs are a
natural generalizati... | computer science |
3,077 | A Classification Refinement Strategy for Semantic Segmentation | cs.CV | Based on the observation that semantic segmentation errors are partially
predictable, we propose a compact formulation using confusion statistics of the
trained classifier to refine (re-estimate) the initial pixel label hypotheses.
The proposed strategy is contingent upon computing the classifier confusion
probabilitie... | computer science |
3,078 | 3D Object Dense Reconstruction from a Single Depth View | cs.CV | In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs
the complete 3D structure of a given object from a single arbitrary depth view
using generative adversarial networks. Unlike existing work which typically
requires multiple views of the same object or class labels to recover the full
3D geometr... | computer science |
3,079 | Intriguing Properties of Randomly Weighted Networks: Generalizing While
Learning Next to Nothing | cs.LG | Training deep neural networks results in strong learned representations that
show good generalization capabilities. In most cases, training involves
iterative modification of all weights inside the network via back-propagation.
In Extreme Learning Machines, it has been suggested to set the first layer of a
network to f... | computer science |
3,080 | One-Shot Imitation from Observing Humans via Domain-Adaptive
Meta-Learning | cs.LG | Humans and animals are capable of learning a new behavior by observing others
perform the skill just once. We consider the problem of allowing a robot to do
the same -- learning from a raw video pixels of a human, even when there is
substantial domain shift in the perspective, environment, and embodiment
between the ro... | computer science |
3,081 | Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks | cs.CV | The robust and efficient recognition of visual relations in images is a
hallmark of biological vision. Here, we argue that, despite recent progress in
visual recognition, modern machine vision algorithms are severely limited in
their ability to learn visual relations. Through controlled experiments, we
demonstrate that... | computer science |
3,082 | Local Contrast Learning | cs.LG | Learning a deep model from small data is yet an opening and challenging
problem. We focus on one-shot classification by deep learning approach based on
a small quantity of training samples. We proposed a novel deep learning
approach named Local Contrast Learning (LCL) based on the key insight about a
human cognitive be... | computer science |
3,083 | Challenging Images For Minds and Machines | cs.LG | There is no denying the tremendous leap in the performance of machine
learning methods in the past half-decade. Some might even say that specific
sub-fields in pattern recognition, such as machine-vision, are as good as
solved, reaching human and super-human levels. Arguably, lack of training data
and computation power... | computer science |
3,084 | Bridging Cognitive Programs and Machine Learning | cs.LG | While great advances are made in pattern recognition and machine learning,
the successes of such fields remain restricted to narrow applications and seem
to break down when training data is scarce, a shift in domain occurs, or when
intelligent reasoning is required for rapid adaptation to new environments. In
this work... | computer science |
3,085 | Explanations based on the Missing: Towards Contrastive Explanations with
Pertinent Negatives | cs.AI | In this paper we propose a novel method that provides contrastive
explanations justifying the classification of an input by a black box
classifier such as a deep neural network. Given an input we find what should be
minimally and sufficiently present (viz. important object pixels in an image)
to justify its classificat... | computer science |
3,086 | Semi-parametric Topological Memory for Navigation | cs.LG | We introduce a new memory architecture for navigation in previously unseen
environments, inspired by landmark-based navigation in animals. The proposed
semi-parametric topological memory (SPTM) consists of a (non-parametric) graph
with nodes corresponding to locations in the environment and a (parametric)
deep network ... | computer science |
3,087 | Clustering with Simultaneous Local and Global View of Data: A message
passing based approach | cs.LG | A good clustering algorithm should not only be able to discover clusters of
arbitrary shapes (global view) but also provide additional information, which
can be used to gain more meaningful insights into the internal structure of the
clusters (local view). In this work we use the mathematical framework of factor
graphs... | computer science |
3,088 | Learning to Cluster for Proposal-Free Instance Segmentation | cs.CV | This work proposed a novel learning objective to train a deep neural network
to perform end-to-end image pixel clustering. We applied the approach to
instance segmentation, which is at the intersection of image semantic
segmentation and object detection. We utilize the most fundamental property of
instance labeling -- ... | computer science |
3,089 | Stacked Cross Attention for Image-Text Matching | cs.CV | In this paper, we study the problem of image-text matching. Inferring the
latent semantic alignment between objects or other salient stuffs (e.g. snow,
sky, lawn) and the corresponding words in sentences allows to capture
fine-grained interplay between vision and language, and makes image-text
matching more interpretab... | computer science |
3,090 | Text2Shape: Generating Shapes from Natural Language by Learning Joint
Embeddings | cs.CV | We present a method for generating colored 3D shapes from natural language.
To this end, we first learn joint embeddings of freeform text descriptions and
colored 3D shapes. Our model combines and extends learning by association and
metric learning approaches to learn implicit cross-modal connections, and
produces a jo... | computer science |
3,091 | Hierarchical Clustering for Finding Symmetries and Other Patterns in
Massive, High Dimensional Datasets | stat.ML | Data analysis and data mining are concerned with unsupervised pattern finding
and structure determination in data sets. "Structure" can be understood as
symmetry and a range of symmetries are expressed by hierarchy. Such symmetries
directly point to invariants, that pinpoint intrinsic properties of the data
and of the ... | computer science |
3,092 | Clustering with Multi-Layer Graphs: A Spectral Perspective | cs.LG | Observational data usually comes with a multimodal nature, which means that
it can be naturally represented by a multi-layer graph whose layers share the
same set of vertices (users) with different edges (pairwise relationships). In
this paper, we address the problem of combining different layers of the
multi-layer gra... | computer science |
3,093 | Ground Metric Learning | stat.ML | Transportation distances have been used for more than a decade now in machine
learning to compare histograms of features. They have one parameter: the ground
metric, which can be any metric between the features themselves. As is the case
for all parameterized distances, transportation distances can only prove useful
in... | computer science |
3,094 | Sparse Image Representation with Epitomes | cs.LG | Sparse coding, which is the decomposition of a vector using only a few basis
elements, is widely used in machine learning and image processing. The basis
set, also called dictionary, is learned to adapt to specific data. This
approach has proven to be very effective in many image processing tasks.
Traditionally, the di... | computer science |
3,095 | Multi-criteria Anomaly Detection using Pareto Depth Analysis | cs.LG | We consider the problem of identifying patterns in a data set that exhibit
anomalous behavior, often referred to as anomaly detection. In most anomaly
detection algorithms, the dissimilarity between data samples is calculated by a
single criterion, such as Euclidean distance. However, in many cases there may
not exist ... | computer science |
3,096 | On the Lagrangian Biduality of Sparsity Minimization Problems | cs.CV | Recent results in Compressive Sensing have shown that, under certain
conditions, the solution to an underdetermined system of linear equations with
sparsity-based regularization can be accurately recovered by solving convex
relaxations of the original problem. In this work, we present a novel
primal-dual analysis on a ... | computer science |
3,097 | Hybrid Generative/Discriminative Learning for Automatic Image Annotation | cs.LG | Automatic image annotation (AIA) raises tremendous challenges to machine
learning as it requires modeling of data that are both ambiguous in input and
output, e.g., images containing multiple objects and labeled with multiple
semantic tags. Even more challenging is that the number of candidate tags is
usually huge (as ... | computer science |
3,098 | Automatic Tuning of Interactive Perception Applications | cs.LG | Interactive applications incorporating high-data rate sensing and computer
vision are becoming possible due to novel runtime systems and the use of
parallel computation resources. To allow interactive use, such applications
require careful tuning of multiple application parameters to meet required
fidelity and latency ... | computer science |
3,099 | Robust Nonnegative Matrix Factorization via $L_1$ Norm Regularization | cs.LG | Nonnegative Matrix Factorization (NMF) is a widely used technique in many
applications such as face recognition, motion segmentation, etc. It
approximates the nonnegative data in an original high dimensional space with a
linear representation in a low dimensional space by using the product of two
nonnegative matrices. ... | computer science |
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