385 citations · 1.5k across the 36 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.CV2019
Seeing What a GAN Cannot Generate
David Bau, Jun-Yan Zhu, Jonas Wulff +4
Despite the success of Generative Adversarial Networks (GANs), mode collapse remains a serious issue during GAN training. To date, little work has focused on understanding and quan…
cs.LG2019★ 2 cited
Dissecting Pruned Neural Networks
Jonathan Frankle, David Bau
Pruning is a standard technique for removing unnecessary structure from a neural network to reduce its storage footprint, computational demands, or energy consumption. Pruning can…
cs.LG2019★ 11 cited
On the Units of GANs (Extended Abstract)
David Bau, Jun-Yan Zhu, Hendrik Strobelt +4
Generative Adversarial Networks (GANs) have achieved impressive results for many real-world applications. As an active research topic, many GAN variants have emerged with improveme…