Matthias Bethge

research

∙ 07/10/2023

Compositional Generalization from First Principles

Leveraging the compositional nature of our world to expedite learning an...

0 Thaddäus Wiedemer, et al. ∙

research

∙ 06/08/2023

RDumb: A simple approach that questions our progress in continual test-time adaptation

Test-Time Adaptation (TTA) allows to update pretrained models to changin...

0 Ori Press, et al. ∙

research

∙ 05/26/2023

Playing repeated games with Large Language Models

Large Language Models (LLMs) are transforming society and permeating int...

8 Elif Akata, et al. ∙

research

∙ 02/26/2023

Invariant Neural Ordinary Differential Equations

Latent neural ordinary differential equations have been proven useful fo...

1 Ilze Amanda Auzina, et al. ∙

research

∙ 12/29/2021

Disentanglement and Generalization Under Correlation Shifts

Correlations between factors of variation are prevalent in real-world da...

16 Christina M. Funke, et al. ∙

research

∙ 10/13/2021

Unsupervised Object Learning via Common Fate

Learning generative object models from unlabelled videos is a long stand...

7 Matthias Tangemann, 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

∙ 05/26/2021

Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling

Since 2014 transfer learning has become the key driver for the improveme...

11 Akis Linardos, 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

∙ 02/24/2021

State-of-the-Art in Human Scanpath Prediction

The last years have seen a surge in models predicting the scanpaths of f...

25 Matthias Kümmerer, 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

∙ 11/09/2020

Closing the Generalization Gap in One-Shot Object Detection

Despite substantial progress in object detection and few-shot learning, ...

0 Claudio Michaelis, 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

∙ 07/15/2020

Fast Differentiable Clipping-Aware Normalization and Rescaling

Rescaling a vector δ⃗∈ℝ^n to a desired length is a common operation in m...

6 Jonas Rauber, 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

∙ 04/27/2020

Towards causal generative scene models via competition of experts

Learning how to model complex scenes in a modular way with recombinable ...

4 Julius von Kügelgen, 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

∙ 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

∙ 09/24/2019

Pretraining boosts out-of-domain robustness for pose estimation

Deep neural networks are highly effective tools for human and animal pos...

1 Alexander Mathis, 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

∙ 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

∙ 11/28/2018

One-Shot Instance Segmentation

We tackle one-shot visual search by example for arbitrary object categor...

16 Claudio Michaelis, et al. ∙

research

∙ 11/01/2018

Excessive Invariance Causes Adversarial Vulnerability

Despite their impressive performance, deep neural networks exhibit strik...

16 Jörn-Henrik Jacobsen, et al. ∙

research

∙ 09/27/2018

A rotation-equivariant convolutional neural network model of primary visual cortex

Classical models describe primary visual cortex (V1) as a filter bank of...

6 Alexander S. Ecker, et al. ∙

research

∙ 08/27/2018

Generalisation in humans and deep neural networks

We compare the robustness of humans and current convolutional deep neura...

4 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

∙ 07/27/2018

Diverse feature visualizations reveal invariances in early layers of deep neural networks

Visualizing features in deep neural networks (DNNs) can help understandi...

2 Santiago A. Cadena, 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

∙ 04/09/2018

Markerless tracking of user-defined features with deep learning

Quantifying behavior is crucial for many applications in neuroscience. V...

2 Alexander Mathis, et al. ∙

research

∙ 03/26/2018

One-Shot Segmentation in Clutter

We tackle the problem of one-shot segmentation: finding and segmenting a...

0 Claudio Michaelis, 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

∙ 12/18/2017

Guiding human gaze with convolutional neural networks

The eye fixation patterns of human observers are a fundamental indicator...

0 Leon A. Gatys, 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

∙ 11/07/2017

Neural system identification for large populations separating "what" and "where"

Neuroscientists classify neurons into different types that perform simil...

0 David A. Klindt, 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

∙ 06/21/2017

Comparing deep neural networks against humans: object recognition when the signal gets weaker

Human visual object recognition is typically rapid and seemingly effortl...

0 Robert Geirhos, 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

∙ 02/22/2017

Synthesising Dynamic Textures using Convolutional Neural Networks

Here we present a parametric model for dynamic textures. The model is ba...

0 Christina M. Funke, et al. ∙

research

∙ 11/23/2016

Controlling Perceptual Factors in Neural Style Transfer

Neural Style Transfer has shown very exciting results enabling new forms...

0 Leon A. Gatys, et al. ∙

research

∙ 10/05/2016

DeepGaze II: Reading fixations from deep features trained on object recognition

Here we present DeepGaze II, a model that predicts where people look in ...

0 Matthias Kümmerer, et al. ∙

Matthias Bethge

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