4.3k citations · 15.4k across the 23 of their papers we have counts for
3 papers · 1 filter
Adversarial Feature Learning
Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions ha…
Context Encoders: Feature Learning by Inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue +2
We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -- a convolutional…
Generating Visual Explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach +3
Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generall…