David Bau

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MIT

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

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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

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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

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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

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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

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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

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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

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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

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Dissecting Pruned Neural Networks

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

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David Bau

Featured Co-authors

Joshua B. Tenenbaum

Antonio Torralba

William T. Freeman

Bolei Zhou

Jun-Yan Zhu

Aude Oliva

Hendrik Strobelt

Aditya Khosla

Jonathan Frankle

Lalana Kagal

Jonas Wulff

Seeing What a GAN Cannot Generate

Visualizing and Understanding Generative Adversarial Networks (Extended Abstract)

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Interpreting Deep Visual Representations via Network Dissection

Network Dissection: Quantifying Interpretability of Deep Visual Representations

Learnable Programming: Blocks and Beyond

Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning

Revisiting the Importance of Individual Units in CNNs via Ablation

Dissecting Pruned Neural Networks