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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received lessExpand
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Automatic differentiation in PyTorch
In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably LuaExpand
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Wasserstein Generative Adversarial Networks
We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse,Expand
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Wasserstein GAN
The problem this paper is concerned with is that of unsupervised learning. Mainly, what does it mean to learn a probability distribution? The classical answer to this is to learn a probabilityExpand
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designedExpand
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Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramidExpand
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Pedestrian Detection with Unsupervised Multi-stage Feature Learning
Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art and competitiveExpand
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Convolutional neural networks applied to house numbers digit classification
We classify digits of real-world house numbers using convolutional neural networks (ConvNets). Con-vNets are hierarchical feature learning neural networks whose structure is biologically inspired.Expand
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Semantic Segmentation using Adversarial Networks
Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models.Expand
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Fast Convolutional Nets With fbfft: A GPU Performance Evaluation
We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units. We introduce two new Fast Fourier Transform convolutionExpand
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