Publications with the keyword "deep learning"


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
S. Schneider, E. Rusak, L. Eck, O. Bringmann, W. Brendel, and M. Bethge
Removing covariate shift improves robustness against common corruptions
34th Conference on Neural Information Processing Systems (NeurIPS), 2020
#domain adaptation, #covariate shift, #robustness, #deep learning, #computer vision, #benchmarking
Code, URL, Workshop Paper, BibTex
I. Ustyuzhaninov*, W. Brendel*, L. Gatys, and M. Bethge
What does it take to generate natural textures?
International Conference on Learning Representations, 2017
#deep learning, #texture synthesis, #random networks
Code, URL, PDF, Poster, BibTex
S. Bahrampour, N. Ramakrishnan, L. Schott, and M. Shah
Comparative Study of Deep Learning Software Frameworks
2016
#caffe, #torch, #theano, #neon, #deep learning
Code, URL, BibTex
L. Theis and M. Bethge
Generative Image Modeling Using Spatial LSTMs
Advances in Neural Information Processing Systems 28, 2015
#deep learning, #generative modeling, #natural image statistics, #lstm, #mcgsm
Code, URL, PDF, Supplemental, BibTex
L. A. Gatys, A. S. Ecker, and M. Bethge
Texture Synthesis Using Convolutional Neural Networks
Advances in Neural Information Processing Systems 28, 2015
#texture synthesis, #ventral stream, #convolutional neural networks, #deep learning
Code, URL, PDF, Example textures, BibTex
M. Kümmerer, L. Theis, and M. Bethge
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
ICLR Workshop, 2015
#saliency, #deep learning
URL, PDF, BibTex
L. Theis, S. Gerwinn, F. Sinz, and M. Bethge
In All Likelihood, Deep Belief Is Not Enough
Journal of Machine Learning Research, 12, 3071-3096, 2011
#natural image statistics, #deep belief networks, #boltzmann machines, #deep learning
Code, PDF, BibTex
University of Tuebingen BCCN CIN MPI