4.6k citations · 4.6k across the 2 of their papers we have counts for
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stat.ML2014★ 13 cited
Self-informed neural network structure learning
David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov
We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier…
stat.ML2014★ 4.6k cited
Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza +5
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data dis…