Topic Tag: CIFAR

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 Non-Parametric Transformation Networks

  

ConvNets have been very effective in many applications where it is required to learn invariances to within-class nuisance transformations. However, through their architecture, ConvNets only enforce invariance to translation. In this paper, we introduce a new class of convolutional architectures cal…


 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…


 Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

   

Major winning Convolutional Neural Networks (CNNs), such as AlexNet, VGGNet, ResNet, GoogleNet, include tens to hundreds of millions of parameters, which impose considerable computation and memory overhead. This limits their practical use for training, optimization and memory efficiency. On the con…


 Introspective Classification with Convolutional Nets

   

We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowered with generative capabilities. We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory. Our ICN trie…


 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…


 PixelSNAIL: An Improved Autoregressive Generative Model

    

Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the …


 Tensor Regression Networks with various Low-Rank Tensor Approximations

Tensor regression networks achieve high rate of compression of model parameters in multilayer perceptrons (MLP) while having slight impact on performances. Tensor regression layer imposes low-rank constraints on the tensor regression layer which replaces the flattening operation of traditional MLP.…


 Exploring the Space of Black-box Attacks on Deep Neural Networks

   

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 att…


 Learning in the Machine: Random Backpropagation and the Deep Learning Channel

  

Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using …


 Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

  

The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, becaus…


 Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

    

We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor T…


 Generalization in Deep Learning

  

This paper explains why deep learning can generalize well, despite large capacity and possible algorithmic instability, nonrobustness, and sharp minima, effectively addressing an open problem in the literature. Based on our theoretical insight, this paper also proposes a family of new regularizatio…


 A systematic study of the class imbalance problem in convolutional neural networks

    

In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine l…


 An Effective Training Method For Deep Convolutional Neural Network

 

In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks. The proposed method modifies a family of activation functions as nonlinearity generators (NGs). NGs make the activation functions linear symmetric for their inp…


 Projection Based Weight Normalization for Deep Neural Networks

      

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-n…


 Deep Convolutional Neural Networks as Generic Feature Extractors

 

Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this task. However, the long time needed to train su…


 Generative Adversarial Mapping Networks

   

Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as…


 Confident Multiple Choice Learning

  

Ensemble methods are arguably the most trustworthy techniques for boosting the performance of machine learning models. Popular independent ensembles (IE) relying on naive averaging/voting scheme have been of typical choice for most applications involving deep neural networks, but they do not consid…


 Neural Optimizer Search with Reinforcement Learning

      

We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathematical update equation based on a list of primiti…


 Class-Splitting Generative Adversarial Networks

  

Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i.e. in the conditional GAN setup. This is still observed for the recently proposed Wasserstein GAN formulation which stabilized adversarial training and allows considerin…


 Multi-Generator Generative Adversarial Nets

  

We propose in this paper a novel approach to address the mode collapse problem in Generative Adversarial Nets (GANs) by training many generators. The training procedure is formulated as a minimax game among many generators, a classifier, and a discriminator. Generators produce data to fool the disc…


 Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning

  

In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contri…


 Adversarial Dropout for Supervised and Semi-supervised Learning

 

Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs to enhance the generality, this paper introd…


 Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification

      

Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppo…


 A Learning Approach to Secure Learning

   

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…