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07/10/2023
Compositional Generalization from First Principles
Leveraging the compositional nature of our world to expedite learning an...
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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...
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05/26/2023
Playing repeated games with Large Language Models
Large Language Models (LLMs) are transforming society and permeating int...
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02/26/2023
Invariant Neural Ordinary Differential Equations
Latent neural ordinary differential equations have been proven useful fo...
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12/29/2021
Disentanglement and Generalization Under Correlation Shifts
Correlations between factors of variation are prevalent in real-world da...
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10/13/2021
Unsupervised Object Learning via Common Fate
Learning generative object models from unlabelled videos is a long stand...
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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...
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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...
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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. ...
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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...
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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...
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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...
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02/17/2021
Contrastive Learning Inverts the Data Generating Process
Contrastive learning has recently seen tremendous success in self-superv...
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11/09/2020
Closing the Generalization Gap in One-Shot Object Detection
Despite substantial progress in object detection and few-shot learning, ...
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10/23/2020
Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations
Feature visualizations such as synthetic maximally activating images are...
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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...
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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...
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07/21/2020
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
We construct an unsupervised learning model that achieves nonlinear dise...
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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...
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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...
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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...
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04/27/2020
Towards causal generative scene models via competition of experts
Learning how to model complex scenes in a modular way with recombinable ...
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04/20/2020
The Notorious Difficulty of Comparing Human and Machine Perception
With the rise of machines to human-level performance in complex recognit...
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04/16/2020
Shortcut Learning in Deep Neural Networks
Deep learning has triggered the current rise of artificial intelligence ...
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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...
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11/11/2019
Learning From Brains How to Regularize Machines
Despite impressive performance on numerous visual tasks, Convolutional N...
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09/24/2019
Pretraining boosts out-of-domain robustness for pose estimation
Deep neural networks are highly effective tools for human and animal pos...
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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...
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07/01/2019
Accurate, reliable and fast robustness evaluation
Throughout the past five years, the susceptibility of neural networks to...
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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...
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11/28/2018
One-Shot Instance Segmentation
We tackle one-shot visual search by example for arbitrary object categor...
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11/01/2018
Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit strik...
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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...
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08/27/2018
Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neura...
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08/06/2018
Adversarial Vision Challenge
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...
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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...
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07/07/2018
One-shot Texture Segmentation
We introduce one-shot texture segmentation: the task of segmenting an in...
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05/23/2018
Robust Perception through Analysis by Synthesis
The intriguing susceptibility of deep neural networks to minimal input p...
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04/09/2018
Markerless tracking of user-defined features with deep learning
Quantifying behavior is crucial for many applications in neuroscience. V...
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03/26/2018
One-Shot Segmentation in Clutter
We tackle the problem of one-shot segmentation: finding and segmenting a...
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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 ...
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12/18/2017
Guiding human gaze with convolutional neural networks
The eye fixation patterns of human observers are a fundamental indicator...
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12/12/2017
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Many machine learning algorithms are vulnerable to almost imperceptible ...
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11/07/2017
Neural system identification for large populations separating "what" and "where"
Neuroscientists classify neurons into different types that perform simil...
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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...
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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...
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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 ...
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02/22/2017
Synthesising Dynamic Textures using Convolutional Neural Networks
Here we present a parametric model for dynamic textures. The model is ba...
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11/23/2016
Controlling Perceptual Factors in Neural Style Transfer
Neural Style Transfer has shown very exciting results enabling new forms...
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10/05/2016
