David Bau

is this you? claim profile

0 followers
MIT

Diverse Image Generation via Self-Conditioned GANs

We introduce a simple but effective unsupervised method for generating r...
06/18/2020 ∙ by Steven Liu, et al. ∙ 12 ∙ share

read it
Semantic Photo Manipulation with a Generative Image Prior

Despite the recent success of GANs in synthesizing images conditioned on...
05/15/2020 ∙ by David Bau, et al. ∙ 21 ∙ share

read it
Seeing What a GAN Cannot Generate

Despite the success of Generative Adversarial Networks (GANs), mode coll...
10/24/2019 ∙ by David Bau, et al. ∙ 52 ∙ share

read it
Dissecting Pruned Neural Networks

Pruning is a standard technique for removing unnecessary structure from ...
06/29/2019 ∙ by Jonathan Frankle, et al. ∙ 0 ∙ share

read it
Visualizing and Understanding Generative Adversarial Networks (Extended Abstract)

Generative Adversarial Networks (GANs) have achieved impressive results ...
01/29/2019 ∙ by David Bau, et al. ∙ 22 ∙ share

read it
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Generative Adversarial Networks (GANs) have recently achieved impressive...
11/26/2018 ∙ by David Bau, et al. ∙ 10 ∙ share

read it
Revisiting the Importance of Individual Units in CNNs via Ablation

We revisit the importance of the individual units in Convolutional Neura...
06/07/2018 ∙ by Bolei Zhou, et al. ∙ 0 ∙ share

read it
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning

There has recently been a surge of work in explanatory artificial intell...
05/31/2018 ∙ by Leilani H. Gilpin, et al. ∙ 0 ∙ share

read it
Interpreting Deep Visual Representations via Network Dissection

The success of recent deep convolutional neural networks (CNNs) depends ...
11/15/2017 ∙ by Bolei Zhou, et al. ∙ 0 ∙ share

read it
Learnable Programming: Blocks and Beyond

Blocks-based programming has become the lingua franca for introductory c...
05/26/2017 ∙ by David Bau, et al. ∙ 0 ∙ share

read it
Network Dissection: Quantifying Interpretability of Deep Visual Representations

We propose a general framework called Network Dissection for quantifying...
04/19/2017 ∙ by David Bau, et al. ∙ 0 ∙ share

read it

David Bau

Featured Co-authors

Joshua B. Tenenbaum

Antonio Torralba

Bolei Zhou

William T. Freeman

Jun-Yan Zhu

Aude Oliva

Hendrik Strobelt

Aditya Khosla

Jonathan Frankle

Lalana Kagal

Tongzhou Wang

Diverse Image Generation via Self-Conditioned GANs

Semantic Photo Manipulation with a Generative Image Prior

Seeing What a GAN Cannot Generate

Dissecting Pruned Neural Networks

Visualizing and Understanding Generative Adversarial Networks (Extended Abstract)

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Revisiting the Importance of Individual Units in CNNs via Ablation

Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning

Interpreting Deep Visual Representations via Network Dissection

Learnable Programming: Blocks and Beyond

Network Dissection: Quantifying Interpretability of Deep Visual Representations