149 citations · 339 across the 12 of their papers we have counts for
8 papers · 1 filter
On the surprising similarities between supervised and self-supervised models
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus +3
How do humans learn to acquire a powerful, flexible and robust representation of objects? While much of this process remains unknown, it is clear that humans do not require million…
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy
Jonas Rauber, Matthias Bethge, Wieland Brendel
EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch, TensorFlow, JAX, and NumPy. Library developers no longer need to choose betwe…
Fast Differentiable Clipping-Aware Normalization and Rescaling
Jonas Rauber, Matthias Bethge
Rescaling a vector to a desired length is a common operation in many areas such as data science and machine learning. When the rescaled perturbation $η\vec…
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
David Klindt, Lukas Schott, Yash Sharma +4
We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos. Previous work suggests that represent…
Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck +3
Today's state-of-the-art machine vision models are vulnerable to image corruptions like blurring or compression artefacts, limiting their performance in many real-world application…
Towards causal generative scene models via competition of experts
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler +2
Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world. However, current…