Wieland Brendel

research

∙ 05/23/2023

Provably Learning Object-Centric Representations

Learning structured representations of the visual world in terms of obje...

0 Jack Brady, et al. ∙

research

∙ 06/28/2022

Increasing Confidence in Adversarial Robustness Evaluations

Hundreds of defenses have been proposed to make deep neural networks rob...

0 Roland S. Zimmermann, et al. ∙

research

∙ 06/06/2022

Embrace the Gap: VAEs Perform Independent Mechanism Analysis

Variational autoencoders (VAEs) are a popular framework for modeling com...

19 Patrik Reizinger, et al. ∙

research

∙ 07/17/2021

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

An important component for generalization in machine learning is to unco...

14 Lukas Schott, et al. ∙

research

∙ 06/23/2021

How Well do Feature Visualizations Support Causal Understanding of CNN Activations?

One widely used approach towards understanding the inner workings of dee...

26 Roland S. Zimmermann, et al. ∙

research

∙ 06/14/2021

Partial success in closing the gap between human and machine vision

A few years ago, the first CNN surpassed human performance on ImageNet. ...

0 Robert Geirhos, et al. ∙

research

∙ 06/08/2021

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Self-supervised representation learning has shown remarkable success in ...

11 Julius von Kügelgen, et al. ∙

research

∙ 04/27/2021

Adapting ImageNet-scale models to complex distribution shifts with self-learning

While self-learning methods are an important component in many recent do...

0 Evgenia Rusak, et al. ∙

research

∙ 02/25/2021

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints

Evaluating adversarial robustness amounts to finding the minimum perturb...

14 Maura Pintor, et al. ∙

research

∙ 02/17/2021

Contrastive Learning Inverts the Data Generating Process

Contrastive learning has recently seen tremendous success in self-superv...

8 Roland S. Zimmermann, et al. ∙

research

∙ 10/23/2020

Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations

Feature visualizations such as synthetic maximally activating images are...

13 Judy Borowski, et al. ∙

research

∙ 10/16/2020

On the surprising similarities between supervised and self-supervised models

How do humans learn to acquire a powerful, flexible and robust represent...

0 Robert Geirhos, et al. ∙

research

∙ 08/10/2020

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

EagerPy is a Python framework that lets you write code that automaticall...

30 Jonas Rauber, et al. ∙

research

∙ 07/21/2020

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

We construct an unsupervised learning model that achieves nonlinear dise...

5 David Klindt, et al. ∙

research

∙ 06/30/2020

Improving robustness against common corruptions by covariate shift adaptation

Today's state-of-the-art machine vision models are vulnerable to image c...

5 Steffen Schneider, et al. ∙

research

∙ 06/12/2020

Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences

Perceiving the world in terms of objects is a crucial prerequisite for r...

14 Marissa A. Weis, et al. ∙

research

∙ 04/20/2020

The Notorious Difficulty of Comparing Human and Machine Perception

With the rise of machines to human-level performance in complex recognit...

8 Christina M. Funke, et al. ∙

research

∙ 04/16/2020

Shortcut Learning in Deep Neural Networks

Deep learning has triggered the current rise of artificial intelligence ...

3 Robert Geirhos, et al. ∙

research

∙ 02/19/2020

On Adaptive Attacks to Adversarial Example Defenses

Adaptive attacks have (rightfully) become the de facto standard for eval...

9 Florian Tramèr, et al. ∙

research

∙ 01/16/2020

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...

14 Evgenia Rusak, et al. ∙

research

∙ 11/11/2019

Learning From Brains How to Regularize Machines

Despite impressive performance on numerous visual tasks, Convolutional N...

36 Zhe Li, et al. ∙

research

∙ 07/17/2019

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

The ability to detect objects regardless of image distortions or weather...

4 Claudio Michaelis, et al. ∙

research

∙ 07/01/2019

Accurate, reliable and fast robustness evaluation

Throughout the past five years, the susceptibility of neural networks to...

3 Wieland Brendel, et al. ∙

research

∙ 02/18/2019

On Evaluating Adversarial Robustness

Correctly evaluating defenses against adversarial examples has proven to...

10 Nicholas Carlini, et al. ∙

research

∙ 11/29/2018

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

Convolutional Neural Networks (CNNs) are commonly thought to recognise o...

14 Robert Geirhos, et al. ∙

research

∙ 08/06/2018

Adversarial Vision Challenge

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...

4 Wieland Brendel, et al. ∙

research

∙ 07/07/2018

One-shot Texture Segmentation

We introduce one-shot texture segmentation: the task of segmenting an in...

0 Ivan Ustyuzhaninov, et al. ∙

research

∙ 05/23/2018

Robust Perception through Analysis by Synthesis

The intriguing susceptibility of deep neural networks to minimal input p...

0 Lukas Schott, et al. ∙

research

∙ 03/23/2018

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

An important preprocessing step in most data analysis pipelines aims to ...

0 Alexander Böttcher, et al. ∙

research

∙ 12/12/2017

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

Many machine learning algorithms are vulnerable to almost imperceptible ...

0 Wieland Brendel, et al. ∙

research

∙ 07/13/2017

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...

0 Jonas Rauber, et al. ∙

research

∙ 04/05/2017

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

A recent paper suggests that Deep Neural Networks can be protected from ...

0 Wieland Brendel, et al. ∙

research

∙ 05/31/2016

Texture Synthesis Using Shallow Convolutional Networks with Random Filters

Here we demonstrate that the feature space of random shallow convolution...

0 Ivan Ustyuzhaninov, et al. ∙

research

∙ 10/22/2014

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...

0 Dmitry Kobak, et al. ∙

Wieland Brendel

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