| 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 | 13094 | 2017 |
| A note on the evaluation of generative models L Theis, A van den Oord, M Bethge International Conference on Learning Representations, 2016 | 1313 | 2016 |
| Lossy Image Compression with Compressive Autoencoders L Theis, W Shi, A Cunningham, F Huszár International Conference on Learning Representations, 2017 | 1260* | 2017 |
| Photo-realistic single image super-resolution using a generative adversarial network [C] C Ledig, L Theis, F Huszar, J Caballero, A Cunningham, A Acosta Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 750 | 2017 |
| HoloGAN: Unsupervised learning of 3D representations from natural images T Nguyen-Phuoc, C Li, L Theis, C Richardt, YL Yang Proceedings of the IEEE International Conference on Computer Vision, 7588-7597, 2019 | 574 | 2019 |
| Amortised MAP inference for image super-resolution CK Sřnderby, J Caballero, L Theis, W Shi, F Huszár International Conference on Learning Representations, 2016 | 523 | 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 | 514 | 2014 |
| 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 | 511 | 2017 |
| Faster gaze prediction with dense networks and Fisher pruning L Theis, I Korshunova, A Tejani, F Huszár arXiv preprint arXiv:1801.05787, 2018 | 238 | 2018 |
| Generative image modeling using spatial LSTMs L Theis, M Bethge Advances in Neural Information Processing Systems 28, 2015 | 231 | 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 | 224 | 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 | 198 | 2017 |
| 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 | 186 | 2016 |
| Super-resolution using a generative adversarial network W Shi, C Ledig, Z Wang, L Theis, F Huszar US Patent App. 15/706,428, 2018 | 161 | 2018 |
| 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 | 132 | 2018 |
| Proceedings of the IEEE conference on computer vision and pattern recognition C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 111 | 2017 |
| Training end-to-end video processes Z Wang, RD Bishop, F Huszar, L Theis US Patent 10,666,962, 2020 | 105 | 2020 |
| An introduction to neural data compression Y Yang, S Mandt, L Theis Foundations and Trends® in Computer Graphics and Vision 15 (2), 113-200, 2023 | 93 | 2023 |
| Universally Quantized Neural Compression E Agustsson, L Theis Advances in Neural Information Processing Systems, 2020 | 79 | 2020 |
| Addressing delayed feedback for continuous training with neural networks in CTR prediction SI Ktena, A Tejani, L Theis, PK Myana, D Dilipkumar, F Huszár, S Yoo, ... Proceedings of the 13th ACM conference on recommender systems, 187-195, 2019 | 67 | 2019 |
Ferenc HuszárUniversity of CambridgeVerified email at cam.ac.uk
