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Lucas Theis
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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
125772017
A note on the evaluation of generative models
L Theis, A van den Oord, M Bethge
International Conference on Learning Representations, 2016
12892016
Lossy Image Compression with Compressive Autoencoders
L Theis, W Shi, A Cunningham, F Huszár
International Conference on Learning Representations, 2017
1213*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
7152017
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
5492019
Amortised MAP inference for image super-resolution
CK Sřnderby, J Caballero, L Theis, W Shi, F Huszár
International Conference on Learning Representations, 2016
5182016
Deep gaze I: Boosting saliency prediction with feature maps trained on imagenet
M Kümmerer, L Theis, M Bethge
arXiv preprint arXiv:1411.1045, 2014
5032014
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
4812017
Generative image modeling using spatial LSTMs
L Theis, M Bethge
Advances in Neural Information Processing Systems 28, 2015
2302015
Faster gaze prediction with dense networks and Fisher pruning
L Theis, I Korshunova, A Tejani, F Huszár
arXiv preprint arXiv:1801.05787, 2018
2252018
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
2222016
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
1932017
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
1842016
Super-resolution using a generative adversarial network
W Shi, C Ledig, Z Wang, L Theis, F Huszar
US Patent App. 15/706,428, 2018
1562018
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
1292018
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
1082017
Training end-to-end video processes
Z Wang, RD Bishop, F Huszar, L Theis
US Patent 10,666,962, 2020
1052020
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
782023
Universally Quantized Neural Compression
E Agustsson, L Theis
Advances in Neural Information Processing Systems, 2020
752020
Photo-realistic single image super-resolution using a generative adversarial network. arXiv 2016
C Ledig, L Theis, F Huszar, J Caballero, A Cunningham, A Acosta, ...
arXiv preprint arXiv:1609.04802, 2016
662016
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