Wieland Brendel

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Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations

Feature visualizations such as synthetic maximally activating images are...
10/23/2020 ∙ by Judy Borowski, et al. ∙ 13 ∙ share

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On the surprising similarities between supervised and self-supervised models

How do humans learn to acquire a powerful, flexible and robust represent...
10/16/2020 ∙ by Robert Geirhos, et al. ∙ 0 ∙ share

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EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy

EagerPy is a Python framework that lets you write code that automaticall...
08/10/2020 ∙ by Jonas Rauber, et al. ∙ 30 ∙ share

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Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

We construct an unsupervised learning model that achieves nonlinear dise...
07/21/2020 ∙ by David Klindt, et al. ∙ 5 ∙ share

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Improving robustness against common corruptions by covariate shift adaptation

Today's state-of-the-art machine vision models are vulnerable to image c...
06/30/2020 ∙ by Steffen Schneider, et al. ∙ 5 ∙ share

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Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences

Perceiving the world in terms of objects is a crucial prerequisite for r...
06/12/2020 ∙ by Marissa A. Weis, et al. ∙ 14 ∙ share

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The Notorious Difficulty of Comparing Human and Machine Perception

With the rise of machines to human-level performance in complex recognit...
04/20/2020 ∙ by Christina M. Funke, et al. ∙ 8 ∙ share

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Shortcut Learning in Deep Neural Networks

Deep learning has triggered the current rise of artificial intelligence ...
04/16/2020 ∙ by Robert Geirhos, et al. ∙ 3 ∙ share

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On Adaptive Attacks to Adversarial Example Defenses

Adaptive attacks have (rightfully) become the de facto standard for eval...
02/19/2020 ∙ by Florian Tramèr, et al. ∙ 9 ∙ share

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Increasing the robustness of DNNs against image corruptions by playing the Game of Noise

The human visual system is remarkably robust against a wide range of nat...
01/16/2020 ∙ by Evgenia Rusak, et al. ∙ 14 ∙ share

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Learning From Brains How to Regularize Machines

Despite impressive performance on numerous visual tasks, Convolutional N...
11/11/2019 ∙ by Zhe Li, et al. ∙ 36 ∙ share

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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

The ability to detect objects regardless of image distortions or weather...
07/17/2019 ∙ by Claudio Michaelis, et al. ∙ 4 ∙ share

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Accurate, reliable and fast robustness evaluation

Throughout the past five years, the susceptibility of neural networks to...
07/01/2019 ∙ by Wieland Brendel, et al. ∙ 3 ∙ share

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On Evaluating Adversarial Robustness

Correctly evaluating defenses against adversarial examples has proven to...
02/18/2019 ∙ by Nicholas Carlini, et al. ∙ 10 ∙ share

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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Convolutional Neural Networks (CNNs) are commonly thought to recognise o...
11/29/2018 ∙ by Robert Geirhos, et al. ∙ 14 ∙ share

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Adversarial Vision Challenge

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...
08/06/2018 ∙ by Wieland Brendel, et al. ∙ 4 ∙ share

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One-shot Texture Segmentation

We introduce one-shot texture segmentation: the task of segmenting an in...
07/07/2018 ∙ by Ivan Ustyuzhaninov, et al. ∙ 0 ∙ share

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Robust Perception through Analysis by Synthesis

The intriguing susceptibility of deep neural networks to minimal input p...
05/23/2018 ∙ by Lukas Schott, et al. ∙ 0 ∙ share

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Trace your sources in large-scale data: one ring to find them all

An important preprocessing step in most data analysis pipelines aims to ...
03/23/2018 ∙ by Alexander Böttcher, et al. ∙ 0 ∙ share

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Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Many machine learning algorithms are vulnerable to almost imperceptible ...
12/12/2017 ∙ by Wieland Brendel, et al. ∙ 0 ∙ share

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Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models

Even todays most advanced machine learning models are easily fooled by a...
07/13/2017 ∙ by Jonas Rauber, et al. ∙ 0 ∙ share

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Comment on "Biologically inspired protection of deep networks from adversarial attacks"

A recent paper suggests that Deep Neural Networks can be protected from ...
04/05/2017 ∙ by Wieland Brendel, et al. ∙ 0 ∙ share

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Texture Synthesis Using Shallow Convolutional Networks with Random Filters

Here we demonstrate that the feature space of random shallow convolution...
05/31/2016 ∙ by Ivan Ustyuzhaninov, et al. ∙ 0 ∙ share

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Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters

Neurons in higher cortical areas, such as the prefrontal cortex, are kno...
10/22/2014 ∙ by Dmitry Kobak, et al. ∙ 0 ∙ share

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Wieland Brendel

Featured Co-authors

Ian Goodfellow

Matthias Bethge

Nicolas Papernot

Richard Zemel

Andreas Geiger

Zhe Li

Nicholas Carlini

Aleksander Madry

Jörn-Henrik Jacobsen

Florian Tramèr

Dimitris Tsipras

Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations

On the surprising similarities between supervised and self-supervised models

EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Improving robustness against common corruptions by covariate shift adaptation

Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences

The Notorious Difficulty of Comparing Human and Machine Perception

Shortcut Learning in Deep Neural Networks

On Adaptive Attacks to Adversarial Example Defenses

Increasing the robustness of DNNs against image corruptions by playing the Game of Noise

Learning From Brains How to Regularize Machines

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Accurate, reliable and fast robustness evaluation

On Evaluating Adversarial Robustness

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Adversarial Vision Challenge

One-shot Texture Segmentation

Robust Perception through Analysis by Synthesis

Trace your sources in large-scale data: one ring to find them all

Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models

Comment on "Biologically inspired protection of deep networks from adversarial attacks"

Texture Synthesis Using Shallow Convolutional Networks with Random Filters

Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters