237 citations · 355 across the 7 of their papers we have counts for
8 papers
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles
Martin Bjerke, Lukas Schott, Kristopher T. Jensen +3
Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives comb…
Score-Based Generative Classifiers
Roland S. Zimmermann, Lukas Schott, Yang Song +2
The tremendous success of generative models in recent years raises the question whether they can also be used to perform classification. Generative models have been used as adversa…
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
Lukas Schott, Julius von Kügelgen, Frederik Träuble +6
An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the wo…
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…
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…
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…
Learned Watershed: End-to-End Learning of Seeded Segmentation
Steffen Wolf, Lukas Schott, Ullrich Köthe +1
Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly wit…
Comparative Study of Deep Learning Software Frameworks
Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott +1
Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate…