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 Sparsity-based Defense against Adversarial Attacks on Linear Classifiers

    

Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing. However, recent work raises important questions about the robustness of such architectures, by showing that it is possible to induce class…


 Frame-Recurrent Video Super-Resolution

    

Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images. Current state-of-the-art methods process a batch of LR frames to generate…


 Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication

 

Traditional radio systems are strictly co-designed on the lower levels of the OSI stack for compatibility and efficiency. Although this has enabled the success of radio communications, it has also introduced lengthy standardization processes and imposed static allocation of the radio spectrum. Vari…


 A Bio-inspired Collision Detecotr for Small Quadcopter

Sense and avoid capability enables insects to fly versatilely and robustly in dynamic complex environment. Their biological principles are so practical and efficient that inspired we human imitating them in our flying machines. In this paper, we studied a novel bio-inspired collision detector and i…


 A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation

    

We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text. However, compared to general feedforward neural networks, RNNs have feedback loops, which makes it a little hard to understa…


 High Dimensional Spaces, Deep Learning and Adversarial Examples

 

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 topo…


 Conditional Probability Models for Deep Image Compression

 

Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state of the art in image compression. The key challenge in learning such networks is twofold: to deal with quantization, and to control the trade-off between reconstruction error (d…


 GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

   

Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient des…


 Weakly Supervised One-Shot Detection with Attention Siamese Networks

 

We consider the task of weakly supervised one-shot detection. In this task, we attempt to perform a detection task over a set of unseen classes, when training only using weak binary labels that indicate the existence of a class instance in a given example. The model is conditioned on a single exemp…


 Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch

  

Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the u…


 Comparative Study on Generative Adversarial Networks

 

In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision a…


 Introducing the CVPR 2018 Learned Image Compression Challenge

  

Posted by Michele Covell, Research Scientist, Google Research Image compression is critical to digital photography — without it, a 12 megapixel image would take 36 megabytes of storage, making most websites prohibitively large. While the signal-processing community has significantly improved imag…


 DENSER: Deep Evolutionary Network Structured Representation

  

Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation (EC). The algorithm not only searches for the best network topology (e.g., number of layers, type of layers), but also tunes hype…


 TFGAN: A Lightweight Library for Generative Adversarial Networks

     

Posted by Joel Shor, Senior Software Engineer, Machine Perception (Crossposted on the Google Open Source Blog) Training a neural network usually involves defining a loss function, which tells the network how close or far it is from its objective. For example, image classification networks are often…


 Predicting Shot Making in Basketball Learnt from Adversarial Multiagent Trajectories

   

In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain specific knowledge. Although intuitive, recent work in deep learning has shown this approach is p…


 Gradient Regularization Improves Accuracy of Discriminative Models

 

Regularizing the gradient norm of the output of a neural network with respect to its inputs is a powerful technique, first proposed by Drucker & LeCun (1991) who named it Double Backpropagation. The idea has been independently rediscovered several times since then, most often with the goal of m…


 Learning Rapid-Temporal Adaptations

 

A hallmark of human intelligence and cognition is its flexibility. One of the long-standing goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation – the ability t…


 CNN Is All You Need

       

The Convolution Neural Network (CNN) has demonstrated the unique advantage in audio, image and text learning; recently it has also challenged Recurrent Neural Networks (RNNs) with long short-term memory cells (LSTM) in sequence-to-sequence learning, since the computations involved in CNN are easily…


 SLAC: A Sparsely Labeled Dataset for Action Classification and Localization

   

This paper describes a procedure for the creation of large-scale video datasets for action classification and localization from unconstrained, realistic web data. The scalability of the proposed procedure is demonstrated by building a novel video benchmark, named SLAC (Sparsely Labeled ACtions), co…


 Optimal structure and parameter learning of Ising models

Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted towards…


 The Robust Manifold Defense: Adversarial Training using Generative Models

    

Deep neural networks are demonstrating excellent performance on several classical vision problems. However, these networks are vulnerable to adversarial examples, minutely modified images that induce arbitrary attacker-chosen output from the network. We propose a mechanism to protect against these …


 Interpretable Counting for Visual Question Answering

Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classifying answers based on fixed length representations of both the image and question or summing fractional counts estimate…


 Carvana Image Masking Challenge–1st Place Winner’s Interview

  

This year, Carvana, a successful online used car startup, challenged the Kaggle community to develop an algorithm that automatically removes the photo studio background. This would allow Carvana to superimpose cars on a variety of backgrounds. In this winner’s interview, the first place team…


 Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models

 

Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition gene…


 Detection and classification of masses in mammographic images in a multi-kernel approach

  

According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality …