Publications by L. Schott


L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, et al.
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
Code, URL, BibTex

Journal Articles

D. Klindt, L. Schott, Y. Sharma, I. Ustyuzhaninov, W. Brendel, M. Bethge, and D. Paiton
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
International Conference on Learning Representations (ICLR), 2020
#disentanglement, #independent component analysis, #nonlinear, #deep learning, #computer vision, #benchmarking
Code, URL, BibTex

Conference Papers

E. Rusak, L. Schott, R. Zimmermann, J. Bitterwolf, O. Bringmann, M. Bethge, and W. Brendel
A simple way to make neural networks robust against diverse image corruptions
European Conference on Computer Vision (ECCV), 2020
URL, BibTex
L. Schott, J. Rauber, W. Brendel, and M. Bethge
Towards the first adversarially robust neural network model on MNIST
International Conference on Learning Representations (ICLR), 2019
URL, BibTex
S. Wolf, L. Schott, U. Köthe, and F. Hamprecht
Learned watershed: end-to-end learning of seeded segmentation
URL, BibTex
S. Zhang, S. Bahrampour, N. Ramakrishnan, L. Schott, and M. Shah
Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction
Code, PDF, BibTex

Technical Reports

S. Bahrampour, N. Ramakrishnan, L. Schott, and M. Shah
Comparative Study of Deep Learning Software Frameworks
#caffe, #torch, #theano, #neon, #deep learning
Code, URL, BibTex
University of Tuebingen BCCN CIN MPI