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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TLDR
This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning. Expand
Automatic differentiation in PyTorch
TLDR
An automatic differentiation module of PyTorch is described — a library designed to enable rapid research on machine learning models that focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Expand
PyTorch: An Imperative Style, High-Performance Deep Learning Library
TLDR
This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. Expand
Wasserstein Generative Adversarial Networks
TLDR
This work introduces a new algorithm named WGAN, an alternative to traditional GAN training that can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Expand
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
TLDR
A generative parametric model capable of producing high quality samples of natural images using a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. Expand
Pedestrian Detection with Unsupervised Multi-stage Feature Learning
TLDR
This work reports state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model that uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information. Expand
Semantic Segmentation using Adversarial Networks
TLDR
An adversarial training approach to train semantic segmentation models that can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Expand
Convolutional neural networks applied to house numbers digit classification
TLDR
This work augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establishes a new state-of-the-art of 95.10% accuracy on the SVHN dataset (48% error improvement). Expand
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
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
TLDR
The hardware and software infrastructure that supports machine learning at global scale is described, leveraging both GPU and CPU platforms for training and abundant CPU capacity for real-time inference. Expand
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