| Photo-realistic single image super-resolution using a generative adversarial network C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta, ... Computer Vision and Pattern Recognition, 2017 | 2621 | 2017 |
| A note on the evaluation of generative models L Theis, A van den Oord, M Bethge International Conference on Learning Representations, 2016 | 474 | 2016 |
| Deep gaze I: Boosting saliency prediction with feature maps trained on imagenet M Kümmerer, L Theis, M Bethge arXiv preprint arXiv:1411.1045, 2014 | 248 | 2014 |
| Lossy Image Compression with Compressive Autoencoders L Theis, W Shi, A Cunningham, F Huszár International Conference on Learning Representations, 2017 | 227 | 2017 |
| Amortised MAP inference for image super-resolution CK Sønderby, J Caballero, L Theis, W Shi, F Huszár International Conference on Learning Representations, 2016 | 220 | 2016 |
| Generative image modeling using spatial LSTMs L Theis, M Bethge Advances in Neural Information Processing Systems 28, 2015 | 138 | 2015 |
| Benchmarking spike rate inference in population calcium imaging L Theis, P Berens, E Froudarakis, J Reimer, MR Rosón, T Baden, T Euler, ... Neuron 90 (3), 471-482, 2016 | 99* | 2016 |
| Fast face-swap using convolutional neural networks I Korshunova, W Shi, J Dambre, L Theis Proceedings of the IEEE International Conference on Computer Vision, 3677-3685, 2017 | 65 | 2017 |
| Faster gaze prediction with dense networks and Fisher pruning L Theis, I Korshunova, A Tejani, F Huszár arXiv preprint arXiv:1801.05787, 2018 | 45 | 2018 |
| Is the deconvolution layer the same as a convolutional layer? W Shi, J Caballero, L Theis, F Huszar, A Aitken, C Ledig, Z Wang arXiv preprint arXiv:1609.07009, 2016 | 42 | 2016 |
| Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize A Aitken, C Ledig, L Theis, J Caballero, Z Wang, W Shi arXiv preprint arXiv:1707.02937, 2017 | 37 | 2017 |
| Community-based benchmarking improves spike rate inference from two-photon calcium imaging data P Berens, J Freeman, T Deneux, N Chenkov, T McColgan, A Speiser, ... PLoS computational biology 14 (5), e1006157, 2018 | 36 | 2018 |
| In All Likelihood, Deep Belief Is Not Enough L Theis, S Gerwinn, F Sinz, M Bethge Journal of Machine Learning Research 12, 3071-3096, 2011 | 35 | 2011 |
| Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification L Theis, AM Chagas, D Arnstein, C Schwarz, M Bethge PLoS computational biology 9 (11), e1003356, 2013 | 29 | 2013 |
| Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents AM Chagas, L Theis, B Sengupta, MC Stüttgen, M Bethge, C Schwarz Frontiers in Neural Circuits 7, 2013 | 23 | 2013 |
| Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations L Theis, R Hosseini, M Bethge PloS ONE 7 (7), 2012 | 23 | 2012 |
| A trust-region method for stochastic variational inference with applications to streaming data L Theis, MD Hoffman Proceedings of the 32nd International Conference on Machine Learning, 2015 | 18 | 2015 |
| Super-resolution using a generative adversarial network W Shi, C Ledig, Z Wang, L Theis, F Huszar US Patent App. 15/706,428, 2018 | 16 | 2018 |
| HoloGAN: Unsupervised learning of 3D representations from natural images T Nguyen-Phuoc, C Li, L Theis, C Richardt, YL Yang arXiv preprint arXiv:1904.01326, 2019 | 10 | 2019 |
| Training sparse natural image models with a fast Gibbs sampler of an extended state space L Theis, J Sohl-Dickstein, M Bethge Advances in Neural Information Processing Systems 25, 1133-1141, 2012 | 7 | 2012 |
Matthias BethgeCIN, MPI, BCCN TuebingenVerified email at bethgelab.org
