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12,801 | Stabilizing Adversarial Nets With Prediction Methods | cs.LG | Adversarial neural networks solve many important problems in data science,
but are notoriously difficult to train. These difficulties come from the fact
that optimal weights for adversarial nets correspond to saddle points, and not
minimizers, of the loss function. The alternating stochastic gradient methods
typically ... | computer science |
12,802 | DeepMasterPrint: Fingerprint Spoofing via Latent Variable Evolution | cs.CV | Biometric authentication is important for a large range of systems, including
but not limited to consumer electronic devices such as phones. Understanding
the limits of and attacks on such systems is therefore crucial. This paper
presents an attack on fingerprint recognition system using MasterPrints,
synthetic fingerp... | computer science |
12,803 | Visual Semantic Planning using Deep Successor Representations | cs.CV | A crucial capability of real-world intelligent agents is their ability to
plan a sequence of actions to achieve their goals in the visual world. In this
work, we address the problem of visual semantic planning: the task of
predicting a sequence of actions from visual observations that transform a
dynamic environment fr... | computer science |
12,804 | PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured
State Representations | cs.RO | We propose position-velocity encoders (PVEs) which learn---without
supervision---to encode images to positions and velocities of task-relevant
objects. PVEs encode a single image into a low-dimensional position state and
compute the velocity state from finite differences in position. In contrast to
autoencoders, positi... | computer science |
12,805 | Megapixel Size Image Creation using Generative Adversarial Networks | cs.CV | Since its appearance, Generative Adversarial Networks (GANs) have received a
lot of interest in the AI community. In image generation several projects
showed how GANs are able to generate photorealistic images but the results so
far did not look adequate for the quality standard of visual media production
industry. We ... | computer science |
12,806 | Cross-modal Common Representation Learning by Hybrid Transfer Network | cs.MM | DNN-based cross-modal retrieval is a research hotspot to retrieve across
different modalities as image and text, but existing methods often face the
challenge of insufficient cross-modal training data. In single-modal scenario,
similar problem is usually relieved by transferring knowledge from large-scale
auxiliary dat... | computer science |
12,807 | Automatic Response Assessment in Regions of Language Cortex in Epilepsy
Patients Using ECoG-based Functional Mapping and Machine Learning | cs.CV | Accurate localization of brain regions responsible for language and cognitive
functions in Epilepsy patients should be carefully determined prior to surgery.
Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been
shown to be a safer alternative to the electrical cortical stimulation mapping
(ESM... | computer science |
12,808 | Comparative Analysis of Open Source Frameworks for Machine Learning with
Use Case in Single-Threaded and Multi-Threaded Modes | cs.LG | The basic features of some of the most versatile and popular open source
frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are
considered and compared. Their comparative analysis was performed and
conclusions were made as to the advantages and disadvantages of these
platforms. The performance tests... | computer science |
12,809 | Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour | cs.CV | Deep learning thrives with large neural networks and large datasets. However,
larger networks and larger datasets result in longer training times that impede
research and development progress. Distributed synchronous SGD offers a
potential solution to this problem by dividing SGD minibatches over a pool of
parallel wor... | computer science |
12,810 | Large-Scale Plant Classification with Deep Neural Networks | cs.LG | This paper discusses the potential of applying deep learning techniques for
plant classification and its usage for citizen science in large-scale
biodiversity monitoring. We show that plant classification using near
state-of-the-art convolutional network architectures like ResNet50 achieves
significant improvements in ... | computer science |
12,811 | Alternative Semantic Representations for Zero-Shot Human Action
Recognition | cs.CV | A proper semantic representation for encoding side information is key to the
success of zero-shot learning. In this paper, we explore two alternative
semantic representations especially for zero-shot human action recognition:
textual descriptions of human actions and deep features extracted from still
images relevant t... | computer science |
12,812 | Online Convolutional Dictionary Learning | cs.LG | While a number of different algorithms have recently been proposed for
convolutional dictionary learning, this remains an expensive problem. The
single biggest impediment to learning from large training sets is the memory
requirements, which grow at least linearly with the size of the training set
since all existing me... | computer science |
12,813 | Persistence Diagrams with Linear Machine Learning Models | math.AT | Persistence diagrams have been widely recognized as a compact descriptor for
characterizing multiscale topological features in data. When many datasets are
available, statistical features embedded in those persistence diagrams can be
extracted by applying machine learnings. In particular, the ability for
explicitly ana... | computer science |
12,814 | Optimization Beyond the Convolution: Generalizing Spatial Relations with
End-to-End Metric Learning | cs.RO | To operate intelligently in domestic environments, robots require the ability
to understand arbitrary spatial relations between objects and to generalize
them to objects of varying sizes and shapes. In this work, we present a novel
end-to-end approach to generalize spatial relations based on distance metric
learning. W... | computer science |
12,815 | Machine Learning in Appearance-based Robot Self-localization | cs.CV | An appearance-based robot self-localization problem is considered in the
machine learning framework. The appearance space is composed of all possible
images, which can be captured by a robot's visual system under all robot
localizations. Using recent manifold learning and deep learning techniques, we
propose a new geom... | computer science |
12,816 | Discriminative Block-Diagonal Representation Learning for Image
Recognition | cs.CV | Existing block-diagonal representation researches mainly focuses on casting
block-diagonal regularization on training data, while only little attention is
dedicated to concurrently learning both block-diagonal representations of
training and test data. In this paper, we propose a discriminative
block-diagonal low-rank ... | computer science |
12,817 | Deep Learning with Topological Signatures | cs.CV | Inferring topological and geometrical information from data can offer an
alternative perspective on machine learning problems. Methods from topological
data analysis, e.g., persistent homology, enable us to obtain such information,
typically in the form of summary representations of topological features.
However, such ... | computer science |
12,818 | Comparative Performance Analysis of Neural Networks Architectures on H2O
Platform for Various Activation Functions | cs.LG | Deep learning (deep structured learning, hierarchi- cal learning or deep
machine learning) is a branch of machine learning based on a set of algorithms
that attempt to model high- level abstractions in data by using multiple
processing layers with complex structures or otherwise composed of multiple
non-linear transfor... | computer science |
12,819 | Streaming Architecture for Large-Scale Quantized Neural Networks on an
FPGA-Based Dataflow Platform | cs.CV | Deep neural networks (DNNs) are used by different applications that are
executed on a range of computer architectures, from IoT devices to
supercomputers. The footprint of these networks is huge as well as their
computational and communication needs. In order to ease the pressure on
resources, research indicates that i... | computer science |
12,820 | CASSL: Curriculum Accelerated Self-Supervised Learning | cs.RO | Recent self-supervised learning approaches focus on using a few thousand data
points to learn policies for high-level, low-dimensional action spaces.
However, scaling this framework for high-dimensional control require either
scaling up the data collection efforts or using a clever sampling strategy for
training. We pr... | computer science |
12,821 | GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled
Images | cs.LG | We tackle the problem of learning robotic sensorimotor control policies that
can generalize to visually diverse and unseen environments. Achieving broad
generalization typically requires large datasets, which are difficult to obtain
for task-specific interactive processes such as reinforcement learning or
learning from... | computer science |
12,822 | Image Quality Assessment Guided Deep Neural Networks Training | cs.CV | For many computer vision problems, the deep neural networks are trained and
validated based on the assumption that the input images are pristine (i.e.,
artifact-free). However, digital images are subject to a wide range of
distortions in real application scenarios, while the practical issues regarding
image quality in ... | computer science |
12,823 | MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal
Retrieval | cs.MM | Cross-modal retrieval has drawn wide interest for retrieval across different
modalities of data. However, existing methods based on DNN face the challenge
of insufficient cross-modal training data, which limits the training
effectiveness and easily leads to overfitting. Transfer learning is for
relieving the problem of... | computer science |
12,825 | PixelNN: Example-based Image Synthesis | cs.CV | We present a simple nearest-neighbor (NN) approach that synthesizes
high-frequency photorealistic images from an "incomplete" signal such as a
low-resolution image, a surface normal map, or edges. Current state-of-the-art
deep generative models designed for such conditional image synthesis lack two
important things: (1... | computer science |
12,826 | Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms,
and Applications | cs.LG | Recently, convex formulations of low-rank matrix factorization problems have
received considerable attention in machine learning. However, such formulations
often require solving for a matrix of the size of the data matrix, making it
challenging to apply them to large scale datasets. Moreover, in many
applications the ... | computer science |
12,827 | Performance Analysis of Open Source Machine Learning Frameworks for
Various Parameters in Single-Threaded and Multi-Threaded Modes | cs.LG | The basic features of some of the most versatile and popular open source
frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are
considered and compared. Their comparative analysis was performed and
conclusions were made as to the advantages and disadvantages of these
platforms. The performance tests... | computer science |
12,828 | Block-Simultaneous Direction Method of Multipliers: A proximal
primal-dual splitting algorithm for nonconvex problems with multiple
constraints | math.OC | We introduce a generalization of the linearized Alternating Direction Method
of Multipliers to optimize a real-valued function $f$ of multiple arguments
with potentially multiple constraints $g_\circ$ on each of them. The function
$f$ may be nonconvex as long as it is convex in every argument, while the
constraints $g_... | computer science |
12,829 | Telepath: Understanding Users from a Human Vision Perspective in
Large-Scale Recommender Systems | cs.IR | Designing an e-commerce recommender system that serves hundreds of millions
of active users is a daunting challenge. From a human vision perspective,
there're two key factors that affect users' behaviors: items' attractiveness
and their matching degree with users' interests. This paper proposes Telepath,
a vision-based... | computer science |
12,830 | Fast Image Processing with Fully-Convolutional Networks | cs.CV | We present an approach to accelerating a wide variety of image processing
operators. Our approach uses a fully-convolutional network that is trained on
input-output pairs that demonstrate the operator's action. After training, the
original operator need not be run at all. The trained network operates at full
resolution... | computer science |
12,831 | Newton-type Methods for Inference in Higher-Order Markov Random Fields | cs.CV | Linear programming relaxations are central to {\sc map} inference in discrete
Markov Random Fields. The ability to properly solve the Lagrangian dual is a
critical component of such methods. In this paper, we study the benefit of
using Newton-type methods to solve the Lagrangian dual of a smooth version of
the problem.... | computer science |
12,832 | A Comparison on Audio Signal Preprocessing Methods for Deep Neural
Networks on Music Tagging | cs.SD | Deep neural networks (DNN) have been successfully applied for music
classification tasks including music tagging. In this paper, we investigate the
effect of audio preprocessing on music tagging with neural networks. We perform
comprehensive experiments involving audio preprocessing using different
time-frequency repre... | computer science |
12,833 | Multi-modal Conditional Attention Fusion for Dimensional Emotion
Prediction | cs.CV | Continuous dimensional emotion prediction is a challenging task where the
fusion of various modalities usually achieves state-of-the-art performance such
as early fusion or late fusion. In this paper, we propose a novel multi-modal
fusion strategy named conditional attention fusion, which can dynamically pay
attention ... | computer science |
12,834 | A Learning and Masking Approach to Secure Learning | cs.CR | Deep Neural Networks (DNNs) have been shown to be vulnerable against
adversarial examples, which are data points cleverly constructed to fool the
classifier. Such attacks can be devastating in practice, especially as DNNs are
being applied to ever increasing critical tasks like image recognition in
autonomous driving. ... | computer science |
12,835 | From Plants to Landmarks: Time-invariant Plant Localization that uses
Deep Pose Regression in Agricultural Fields | cs.RO | Agricultural robots are expected to increase yields in a sustainable way and
automate precision tasks, such as weeding and plant monitoring. At the same
time, they move in a continuously changing, semi-structured field environment,
in which features can hardly be found and reproduced at a later time.
Challenges for Lid... | computer science |
12,836 | Learning Compact Geometric Features | cs.CV | We present an approach to learning features that represent the local geometry
around a point in an unstructured point cloud. Such features play a central
role in geometric registration, which supports diverse applications in robotics
and 3D vision. Current state-of-the-art local features for unstructured point
clouds h... | computer science |
12,837 | Continuous Multimodal Emotion Recognition Approach for AVEC 2017 | cs.CV | This paper reports the analysis of audio and visual features in predicting
the continuous emotion dimensions under the seventh Audio/Visual Emotion
Challenge (AVEC 2017), which was done as part of a B.Tech. 2nd year internship
project. For visual features we used the HOG (Histogram of Gradients) features,
Fisher encodi... | computer science |
12,838 | Depression Scale Recognition from Audio, Visual and Text Analysis | cs.CV | Depression is a major mental health disorder that is rapidly affecting lives
worldwide. Depression not only impacts emotional but also physical and
psychological state of the person. Its symptoms include lack of interest in
daily activities, feeling low, anxiety, frustration, loss of weight and even
feeling of self-hat... | computer science |
12,839 | Institutionally Distributed Deep Learning Networks | cs.CV | Deep learning has become a promising approach for automated medical
diagnoses. When medical data samples are limited, collaboration among multiple
institutions is necessary to achieve high algorithm performance. However,
sharing patient data often has limitations due to technical, legal, or ethical
concerns. In such ca... | computer science |
12,840 | Temporal Multimodal Fusion for Video Emotion Classification in the Wild | cs.CV | This paper addresses the question of emotion classification. The task
consists in predicting emotion labels (taken among a set of possible labels)
best describing the emotions contained in short video clips. Building on a
standard framework -- lying in describing videos by audio and visual features
used by a supervised... | computer science |
12,841 | Detecting Adversarial Attacks on Neural Network Policies with Visual
Foresight | cs.CV | Deep reinforcement learning has shown promising results in learning control
policies for complex sequential decision-making tasks. However, these neural
network-based policies are known to be vulnerable to adversarial examples. This
vulnerability poses a potentially serious threat to safety-critical systems
such as aut... | computer science |
12,842 | Image Labeling Based on Graphical Models Using Wasserstein Messages and
Geometric Assignment | cs.LG | We introduce a novel approach to Maximum A Posteriori inference based on
discrete graphical models. By utilizing local Wasserstein distances for
coupling assignment measures across edges of the underlying graph, a given
discrete objective function is smoothly approximated and restricted to the
assignment manifold. A co... | computer science |
12,843 | End-to-end Driving via Conditional Imitation Learning | cs.RO | Deep networks trained on demonstrations of human driving have learned to
follow roads and avoid obstacles. However, driving policies trained via
imitation learning cannot be controlled at test time. A vehicle trained
end-to-end to imitate an expert cannot be guided to take a specific turn at an
upcoming intersection. T... | computer science |
12,844 | A New Spectral Clustering Algorithm | cs.LG | We present a new clustering algorithm that is based on searching for natural
gaps in the components of the lowest energy eigenvectors of the Laplacian of a
graph. In comparing the performance of the proposed method with a set of other
popular methods (KMEANS, spectral-KMEANS, and an agglomerative method) in the
context... | computer science |
12,845 | CM-GANs: Cross-modal Generative Adversarial Networks for Common
Representation Learning | cs.MM | It is known that the inconsistent distribution and representation of
different modalities, such as image and text, cause the heterogeneity gap that
makes it challenging to correlate such heterogeneous data. Generative
adversarial networks (GANs) have shown its strong ability of modeling data
distribution and learning d... | computer science |
12,846 | Entanglement Entropy of Target Functions for Image Classification and
Convolutional Neural Network | cs.LG | The success of deep convolutional neural network (CNN) in computer vision
especially image classification problems requests a new information theory for
function of image, instead of image itself. In this article, after establishing
a deep mathematical connection between image classification problem and quantum
spin mo... | computer science |
12,847 | Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural
Networks | cs.LG | With tens of thousands of electrocardiogram (ECG) records processed by mobile
cardiac event recorders every day, heart rhythm classification algorithms are
an important tool for the continuous monitoring of patients at risk. We utilise
an annotated dataset of 12,186 single-lead ECG recordings to build a diverse
ensembl... | computer science |
12,848 | HDR image reconstruction from a single exposure using deep CNNs | cs.CV | Camera sensors can only capture a limited range of luminance simultaneously,
and in order to create high dynamic range (HDR) images a set of different
exposures are typically combined. In this paper we address the problem of
predicting information that have been lost in saturated image areas, in order
to enable HDR rec... | computer science |
12,849 | How to Fool Radiologists with Generative Adversarial Networks? A Visual
Turing Test for Lung Cancer Diagnosis | cs.CV | Discriminating lung nodules as malignant or benign is still an underlying
challenge. To address this challenge, radiologists need computer aided
diagnosis (CAD) systems which can assist in learning discriminative imaging
features corresponding to malignant and benign nodules. However, learning
highly discriminative ima... | computer science |
12,850 | Deep Multi-Modal Classification of Intraductal Papillary Mucinous
Neoplasms (IPMN) with Canonical Correlation Analysis | cs.CV | Pancreatic cancer has the poorest prognosis among all cancer types.
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically
identifiable precursors to pancreatic cancer; hence, early detection and
precise risk assessment of IPMN are vital. In this work, we propose a
Convolutional Neural Network (CNN) base... | computer science |
12,851 | Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio
Source Separation | cs.SD | In deep neural networks with convolutional layers, each layer typically has
fixed-size/single-resolution receptive field (RF). Convolutional layers with a
large RF capture global information from the input features, while layers with
small RF size capture local details with high resolution from the input
features. In t... | computer science |
12,852 | Set-to-Set Hashing with Applications in Visual Recognition | cs.CV | Visual data, such as an image or a sequence of video frames, is often
naturally represented as a point set. In this paper, we consider the
fundamental problem of finding a nearest set from a collection of sets, to a
query set. This problem has obvious applications in large-scale visual
retrieval and recognition, and al... | computer science |
12,853 | Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15
Minutes | cs.DC | We demonstrate that training ResNet-50 on ImageNet for 90 epochs can be
achieved in 15 minutes with 1024 Tesla P100 GPUs. This was made possible by
using a large minibatch size of 32k. To maintain accuracy with this large
minibatch size, we employed several techniques such as RMSprop warm-up, batch
normalization withou... | computer science |
12,854 | Enhanced Attacks on Defensively Distilled Deep Neural Networks | cs.CV | Deep neural networks (DNNs) have achieved tremendous success in many tasks of
machine learning, such as the image classification. Unfortunately, researchers
have shown that DNNs are easily attacked by adversarial examples, slightly
perturbed images which can mislead DNNs to give incorrect classification
results. Such a... | computer science |
12,855 | Verifying Neural Networks with Mixed Integer Programming | cs.LG | Neural networks have demonstrated considerable success in a wide variety of
real-world problems. However, the presence of adversarial examples - slightly
perturbed inputs that are misclassified with high confidence - limits our
ability to guarantee performance for these networks in safety-critical
applications. We demo... | computer science |
12,856 | fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural
Networks on Embedded FPGAs | cs.CV | In recent years, Convolutional Neural Networks (ConvNets) have become an
enabling technology for a wide range of novel embedded Artificial Intelligence
systems. Across the range of applications, the performance needs vary
significantly, from high-throughput video surveillance to the very low-latency
requirements of aut... | computer science |
12,857 | Improving the Adversarial Robustness and Interpretability of Deep Neural
Networks by Regularizing their Input Gradients | cs.LG | Deep neural networks have proven remarkably effective at solving many
classification problems, but have been criticized recently for two major
weaknesses: the reasons behind their predictions are uninterpretable, and the
predictions themselves can often be fooled by small adversarial perturbations.
These problems pose ... | computer science |
12,858 | Scalable Object Detection for Stylized Objects | cs.CV | Following recent breakthroughs in convolutional neural networks and
monolithic model architectures, state-of-the-art object detection models can
reliably and accurately scale into the realm of up to thousands of classes.
Things quickly break down, however, when scaling into the tens of thousands,
or, eventually, to mil... | computer science |
12,859 | Separating Self-Expression and Visual Content in Hashtag Supervision | cs.CV | The variety, abundance, and structured nature of hashtags make them an
interesting data source for training vision models. For instance, hashtags have
the potential to significantly reduce the problem of manual supervision and
annotation when learning vision models for a large number of concepts. However,
a key challen... | computer science |
12,860 | High-Resolution Image Synthesis and Semantic Manipulation with
Conditional GANs | cs.CV | We present a new method for synthesizing high-resolution photo-realistic
images from semantic label maps using conditional generative adversarial
networks (conditional GANs). Conditional GANs have enabled a variety of
applications, but the results are often limited to low-resolution and still far
from realistic. In thi... | computer science |
12,861 | Blind Gain and Phase Calibration via Sparse Spectral Methods | cs.IT | Blind gain and phase calibration (BGPC) is a bilinear inverse problem
involving the determination of unknown gains and phases of the sensing system,
and the unknown signal, jointly. BGPC arises in numerous applications, e.g.,
blind albedo estimation in inverse rendering, synthetic aperture radar
autofocus, and sensor a... | computer science |
12,862 | On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion | cs.IT | In this letter, we study the deterministic sampling patterns for the
completion of low rank matrix, when corrupted with a sparse noise, also known
as robust matrix completion. We extend the recent results on the deterministic
sampling patterns in the absence of noise based on the geometric analysis on
the Grassmannian ... | computer science |
12,863 | Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning | cs.CV | Learning-based pattern classifiers, including deep networks, have
demonstrated impressive performance in several application domains, ranging
from computer vision to computer security. However, it has also been shown that
adversarial input perturbations carefully crafted either at training or at test
time can easily su... | computer science |
12,864 | Training Ensembles to Detect Adversarial Examples | cs.LG | We propose a new ensemble method for detecting and classifying adversarial
examples generated by state-of-the-art attacks, including DeepFool and C&W. Our
method works by training the members of an ensemble to have low classification
error on random benign examples while simultaneously minimizing agreement on
examples ... | computer science |
12,865 | Deep learning enhanced mobile-phone microscopy | cs.LG | Mobile-phones have facilitated the creation of field-portable, cost-effective
imaging and sensing technologies that approach laboratory-grade instrument
performance. However, the optical imaging interfaces of mobile-phones are not
designed for microscopy and produce spatial and spectral distortions in imaging
microscop... | computer science |
12,866 | Sim2Real View Invariant Visual Servoing by Recurrent Control | cs.CV | Humans are remarkably proficient at controlling their limbs and tools from a
wide range of viewpoints and angles, even in the presence of optical
distortions. In robotics, this ability is referred to as visual servoing:
moving a tool or end-point to a desired location using primarily visual
feedback. In this paper, we ... | computer science |
12,867 | Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning
Applications | cs.CR | This paper considers security risks buried in the data processing pipeline in
common deep learning applications. Deep learning models usually assume a fixed
scale for their training and input data. To allow deep learning applications to
handle a wide range of input data, popular frameworks, such as Caffe,
TensorFlow, a... | computer science |
12,868 | Note on Attacking Object Detectors with Adversarial Stickers | cs.CR | Deep learning has proven to be a powerful tool for computer vision and has
seen widespread adoption for numerous tasks. However, deep learning algorithms
are known to be vulnerable to adversarial examples. These adversarial inputs
are created such that, when provided to a deep learning algorithm, they are
very likely t... | computer science |
12,869 | Unifying Map and Landmark Based Representations for Visual Navigation | cs.CV | This works presents a formulation for visual navigation that unifies map
based spatial reasoning and path planning, with landmark based robust plan
execution in noisy environments. Our proposed formulation is learned from data
and is thus able to leverage statistical regularities of the world. This allows
it to efficie... | computer science |
12,870 | Recurrent Pixel Embedding for Instance Grouping | cs.CV | We introduce a differentiable, end-to-end trainable framework for solving
pixel-level grouping problems such as instance segmentation consisting of two
novel components. First, we regress pixels into a hyper-spherical embedding
space so that pixels from the same group have high cosine similarity while
those from differ... | computer science |
12,871 | Exploring the Space of Black-box Attacks on Deep Neural Networks | cs.LG | Existing black-box attacks on deep neural networks (DNNs) so far have largely
focused on transferability, where an adversarial instance generated for a
locally trained model can "transfer" to attack other learning models. In this
paper, we propose novel Gradient Estimation black-box attacks for adversaries
with query a... | computer science |
12,872 | High Dimensional Spaces, Deep Learning and Adversarial Examples | cs.CV | In this paper, we analyze deep learning from a mathematical point of view and
derive several novel results. The results are based on intriguing mathematical
properties of high dimensional spaces. We first look at perturbation based
adversarial examples and show how they can be understood using topological
arguments in ... | computer science |
12,873 | Object segmentation in depth maps with one user click and a
synthetically trained fully convolutional network | cs.CV | With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of ... | computer science |
12,874 | Approximate FPGA-based LSTMs under Computation Time Constraints | cs.CV | Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM)
networks have demonstrated state-of-the-art accuracy in several emerging
Artificial Intelligence tasks. However, the models are becoming increasingly
demanding in terms of computational and memory load. Emerging latency-sensitive
applications inc... | computer science |
12,875 | Characterizing Adversarial Subspaces Using Local Intrinsic
Dimensionality | cs.LG | Deep Neural Networks (DNNs) have recently been shown to be vulnerable against
adversarial examples, which are carefully crafted instances that can mislead
DNNs to make errors during prediction. To better understand such attacks, a
characterization is needed of the properties of regions (the so-called
'adversarial subsp... | computer science |
12,876 | EBIC: an artificial intelligence-based parallel biclustering algorithm
for pattern discovery | cs.LG | In this paper a novel biclustering algorithm based on artificial intelligence
(AI) is introduced. The method called EBIC aims to detect biologically
meaningful, order-preserving patterns in complex data. The proposed algorithm
is probably the first one capable of discovering with accuracy exceeding 50\%
multiple comple... | computer science |
12,877 | Supervised and Unsupervised Tumor Characterization in the Deep Learning
Era | cs.CV | Computer Aided Diagnosis (CAD) tools are often needed for fast and accurate
detection, characterization, and risk assessment of different tumors from
radiology images. Any improvement in robust and accurate image-based tumor
characterization can assist in determining non-invasive cancer stage,
prognosis, and personaliz... | computer science |
12,878 | An octree cells occupancy geometric dimensionality descriptor for
massive on-server point cloud visualisation and classification | cs.CV | Lidar datasets are becoming more and more common. They are appreciated for
their precise 3D nature, and have a wide range of applications, such as surface
reconstruction, object detection, visualisation, etc. For all this
applications, having additional semantic information per point has potential of
increasing the qua... | computer science |
12,879 | VR Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control | cs.RO | This paper deals with the reality gap from a novel perspective, targeting
transferring Deep Reinforcement Learning (DRL) policies learned in simulated
environments to the real-world domain for visual control tasks. Instead of
adopting the common solutions to the problem by increasing the visual fidelity
of synthetic im... | computer science |
12,880 | Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic
Events | eess.SP | One of the risks of large-scale geologic carbon sequestration is the
potential migration of fluids out of the storage formations. Accurate and fast
detection of this fluids migration is not only important but also challenging,
due to the large subsurface uncertainty and complex governing physics.
Traditional leakage de... | computer science |
12,881 | Learning via social awareness: improving sketch representations with
facial feedback | cs.LG | In the quest towards general artificial intelligence (AI), researchers have
explored developing loss functions that act as intrinsic motivators in the
absence of external rewards. This paper argues that such research has
overlooked an important and useful intrinsic motivator: social interaction. We
posit that making an... | computer science |
12,882 | ASP:A Fast Adversarial Attack Example Generation Framework based on
Adversarial Saliency Prediction | cs.CV | With the excellent accuracy and feasibility, the Neural Networks have been
widely applied into the novel intelligent applications and systems. However,
with the appearance of the Adversarial Attack, the NN based system performance
becomes extremely vulnerable:the image classification results can be
arbitrarily misled b... | computer science |
12,883 | Robustness of Rotation-Equivariant Networks to Adversarial Perturbations | cs.CV | Deep neural networks have been shown to be vulnerable to adversarial
examples: very small perturbations of the input having a dramatic impact on the
predictions. A wealth of adversarial attacks and distance metrics to quantify
the similarity between natural and adversarial images have been proposed,
recently enlarging ... | computer science |
12,884 | Global Pose Estimation with an Attention-based Recurrent Network | cs.CV | The ability for an agent to localize itself within an environment is crucial
for many real-world applications. For unknown environments, Simultaneous
Localization and Mapping (SLAM) enables incremental and concurrent building of
and localizing within a map. We present a new, differentiable architecture,
Neural Graph Op... | computer science |
12,885 | How (Not) To Train Your Neural Network Using the Information Bottleneck
Principle | cs.LG | In this theory paper, we investigate training deep neural networks (DNNs) for
classification via minimizing the information bottleneck (IB) functional. We
show that, even if the joint distribution between continuous feature variables
and the discrete class variable is known, the resulting optimization problem
suffers f... | computer science |
12,886 | A Mathematical Framework for Deep Learning in Elastic Source Imaging | math.OC | An inverse elastic source problem with sparse measurements is of concern. A
generic mathematical framework is proposed which incorporates a low-
dimensional manifold regularization in the conventional source reconstruction
algorithms thereby enhancing their performance with sparse datasets. It is
rigorously established... | computer science |
12,887 | Escort: Efficient Sparse Convolutional Neural Networks on GPUs | cs.DC | Deep neural networks have achieved remarkable accuracy in many artificial
intelligence applications, e.g. computer vision, at the cost of a large number
of parameters and high computational complexity. Weight pruning can compress
DNN models by removing redundant parameters in the networks, but it brings
sparsity in the... | computer science |
12,888 | Using Deep Learning for Segmentation and Counting within Microscopy Data | cs.CV | Cell counting is a ubiquitous, yet tedious task that would greatly benefit
from automation. From basic biological questions to clinical trials, cell
counts provide key quantitative feedback that drive research. Unfortunately,
cell counting is most commonly a manual task and can be time-intensive. The
task is made even ... | computer science |
12,889 | Raw Multi-Channel Audio Source Separation using Multi-Resolution
Convolutional Auto-Encoders | cs.SD | Supervised multi-channel audio source separation requires extracting useful
spectral, temporal, and spatial features from the mixed signals. The success of
many existing systems is therefore largely dependent on the choice of features
used for training. In this work, we introduce a novel multi-channel,
multi-resolution... | computer science |
12,890 | Teaching UAVs to Race With Observational Imitation Learning | cs.CV | Recent work has tackled the problem of autonomous navigation by imitating a
teacher and learning an end-to-end policy, which directly predicts controls
from raw images. However, these approaches tend to be sensitive to mistakes by
the teacher and do not scale well to other environments or vehicles. To this
end, we prop... | computer science |
12,891 | Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation
and Augmentation | cs.LG | The results of chest X-ray (CXR) analysis of 2D images to get the
statistically reliable predictions (availability of tuberculosis) by
computer-aided diagnosis (CADx) on the basis of deep learning are presented.
They demonstrate the efficiency of lung segmentation, lossless and lossy data
augmentation for CADx of tuber... | computer science |
12,892 | Occupancy Map Prediction Using Generative and Fully Convolutional
Networks for Vehicle Navigation | cs.LG | Fast, collision-free motion through unknown environments remains a
challenging problem for robotic systems. In these situations, the robot's
ability to reason about its future motion is often severely limited by sensor
field of view (FOV). By contrast, biological systems routinely make decisions
by taking into consider... | computer science |
12,893 | Deep Thermal Imaging: Proximate Material Type Recognition in the Wild
through Deep Learning of Spatial Surface Temperature Patterns | cs.CV | We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neura... | computer science |
12,894 | GONet: A Semi-Supervised Deep Learning Approach For Traversability
Estimation | cs.RO | We present semi-supervised deep learning approaches for traversability
estimation from fisheye images. Our method, GONet, and the proposed extensions
leverage Generative Adversarial Networks (GANs) to effectively predict whether
the area seen in the input image(s) is safe for a robot to traverse. These
methods are trai... | computer science |
12,895 | Revisiting Decomposable Submodular Function Minimization with Incidence
Relations | cs.LG | We introduce a new approach to decomposable submodular function minimization
(DSFM) that exploits incidence relations. Incidence relations describe which
variables effectively influence the component functions, and when properly
utilized, they allow for improving the convergence rates of DSFM solvers. Our
main results ... | computer science |
12,896 | Testing Deep Neural Networks | cs.LG | Deep neural networks (DNNs) have a wide range of applications, and software
employing them must be thoroughly tested, especially in safety critical
domains. However, traditional software testing methodology, including test
coverage criteria and test case generation algorithms, cannot be applied
directly to DNNs. This p... | computer science |
12,897 | Onion-Peeling Outlier Detection in 2-D data Sets | cs.LG | Outlier Detection is a critical and cardinal research task due its array of
applications in variety of domains ranging from data mining, clustering,
statistical analysis, fraud detection, network intrusion detection and
diagnosis of diseases etc. Over the last few decades, distance-based outlier
detection algorithms ha... | computer science |
12,898 | Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey
and Future Directions | cs.CV | In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, re... | computer science |
12,899 | Extended depth-of-field in holographic image reconstruction using deep
learning based auto-focusing and phase-recovery | cs.CV | Holography encodes the three dimensional (3D) information of a sample in the
form of an intensity-only recording. However, to decode the original sample
image from its hologram(s), auto-focusing and phase-recovery are needed, which
are in general cumbersome and time-consuming to digitally perform. Here we
demonstrate a... | computer science |
12,900 | Learning to Rank Scientific Documents from the Crowd | cs.IR | Finding related published articles is an important task in any science, but
with the explosion of new work in the biomedical domain it has become
especially challenging. Most existing methodologies use text similarity metrics
to identify whether two articles are related or not. However biomedical
knowledge discovery is... | computer science |
12,901 | Enabling Embodied Analogies in Intelligent Music Systems | cs.HC | The present methodology is aimed at cross-modal machine learning and uses
multidisciplinary tools and methods drawn from a broad range of areas and
disciplines, including music, systematic musicology, dance, motion capture,
human-computer interaction, computational linguistics and audio signal
processing. Main tasks in... | computer science |
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