3 papers
stat.ML2020
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…
cs.CV2020
A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann +4
The human visual system is remarkably robust against a wide range of naturally occurring variations and corruptions like rain or snow. In contrast, the performance of modern image…
cs.CV2018
Towards the first adversarially robust neural network model on MNIST
Lukas Schott, Jonas Rauber, Matthias Bethge +1
Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neura…