activity
20122016
most citedSemantic Segmentation using Adversarial Networks

489 citations · 1.3k across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2016489 cited

Semantic Segmentation using Adversarial Networks

Pauline Luc, Camille Couprie, Soumith Chintala +1

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 s…

cs.LG201685 cited

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat +5

We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from…

cs.AI2016101 cited

Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks

Nicolas Usunier, Gabriel Synnaeve, Zeming Lin +1

We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms. We propose micromanagement tasks, which present the proble…

cs.LG2014253 cited

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Nicolas Vasilache, Jeff Johnson, Michael Mathieu +3

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 Transfo…

cs.CV2012339 cited

Convolutional Neural Networks Applied to House Numbers Digit Classification

Pierre Sermanet, Soumith Chintala, Yann LeCun

We classify digits of real-world house numbers using convolutional neural networks (ConvNets). ConvNets are hierarchical feature learning neural networks whose structure is biologi…