David Bau

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MIT

Paint by Word

We investigate the problem of zero-shot semantic image painting. Instead...
03/19/2021 ∙ by David Bau, et al. ∙ 6 ∙ share

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Understanding the Role of Individual Units in a Deep Neural Network

Deep neural networks excel at finding hierarchical representations that ...
09/10/2020 ∙ by David Bau, et al. ∙ 11 ∙ share

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What makes fake images detectable? Understanding properties that generalize

The quality of image generation and manipulation is reaching impressive ...
08/24/2020 ∙ by Lucy Chai, et al. ∙ 41 ∙ share

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Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of sema...
07/30/2020 ∙ by David Bau, et al. ∙ 12 ∙ share

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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

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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

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Seeing What a GAN Cannot Generate

Despite the success of Generative Adversarial Networks (GANs), mode coll...
10/24/2019 ∙ by David Bau, et al. ∙ 53 ∙ 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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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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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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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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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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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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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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David Bau

Featured Co-authors

Joshua B. Tenenbaum

Antonio Torralba

Bolei Zhou

William T. Freeman

Ser Nam Lim

Jun-Yan Zhu

Phillip Isola

Aude Oliva

Hendrik Strobelt

Agata Lapedriza

Jonathan Frankle

Paint by Word

Understanding the Role of Individual Units in a Deep Neural Network

What makes fake images detectable? Understanding properties that generalize

Rewriting a Deep Generative Model

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