Publications with the keyword "deep learning"
L. Theis and
J. Ho
Importance weighted compression
Neural Compression Workshop at ICLR, 2021
#compression #deep learning #bits back
URL RIS BibTex
Importance weighted compression
Neural Compression Workshop at ICLR, 2021
#compression #deep learning #bits back
URL RIS BibTex
E. Agustsson and
L. Theis
Universally Quantized Neural Compression
Advances in Neural Information Processing Systems 33, 2020
#compression #deep learning #channel simulation
URL PDF Appendix RIS BibTex
Universally Quantized Neural Compression
Advances in Neural Information Processing Systems 33, 2020
#compression #deep learning #channel simulation
URL PDF Appendix RIS BibTex
T. Nguyen-Phuoc,
C. Li,
L. Theis,
C. Richardt, and
Y.-L. Yang
HoloGAN: Unsupervised learning of 3D representations from natural images
International Conference on Computer Vision, 2019
#generative modeling #3d #deep learning
Code URL PDF Video RIS BibTex
HoloGAN: Unsupervised learning of 3D representations from natural images
International Conference on Computer Vision, 2019
#generative modeling #3d #deep learning
Code URL PDF Video RIS BibTex
I. Korshunova,
W. Shi,
J. Dambre, and
L. Theis
Fast Face-swap Using Convolutional Neural Networks
International Conference on Computer Vision, 2017
#face-swap #cagenet #deep learning
URL RIS BibTex
Fast Face-swap Using Convolutional Neural Networks
International Conference on Computer Vision, 2017
#face-swap #cagenet #deep learning
URL RIS BibTex
C. Ledig,
L. Theis,
F. Huszar,
J. Caballero,
A. Aitken,
A. Tejani,
J. Totz,
Z. Wang,
et al.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Computer Vision and Pattern Recognition, 2017
#super-resolution #deep learning
URL RIS BibTex
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Computer Vision and Pattern Recognition, 2017
#super-resolution #deep learning
URL RIS BibTex
L. Theis,
W. Shi,
A. Cunningham, and
F. Huszár
Lossy Image Compression with Compressive Autoencoders
International Conference on Learning Representations, 2017
#compression #deep learning
URL PDF Poster Files RIS BibTex
Lossy Image Compression with Compressive Autoencoders
International Conference on Learning Representations, 2017
#compression #deep learning
URL PDF Poster Files RIS BibTex
C. Sønderby,
J. Caballero,
L. Theis,
W. Shi, and
F. Huszár
Amortised MAP Inference for Image Super-resolution
International Conference on Learning Representations, 2017
#super-resolution #deep learning
URL RIS BibTex
Amortised MAP Inference for Image Super-resolution
International Conference on Learning Representations, 2017
#super-resolution #deep learning
URL RIS 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 Poster RIS BibTex
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 Poster RIS 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 RIS BibTex
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
ICLR Workshop, 2015
#saliency #deep learning
URL PDF RIS BibTex
L. Theis,
S. Gerwinn,
F. Sinz, and
M. Bethge
In All Likelihood, Deep Belief Is Not Enough
Journal of Machine Learning Research, 12, 2011
#natural image statistics #deep belief networks #boltzmann machines #deep learning
Code PDF RIS BibTex
In All Likelihood, Deep Belief Is Not Enough
Journal of Machine Learning Research, 12, 2011
#natural image statistics #deep belief networks #boltzmann machines #deep learning
Code PDF RIS BibTex