Search Results for author: Bruno Lecouat

Found 12 papers, 8 papers with code

Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts

no code implementations ICCV 2021 Bruno Lecouat, Jean Ponce, Julien Mairal

This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time.

Super-Resolution

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

1 code implementation NeurIPS 2020 Bruno Lecouat, Jean Ponce, Julien Mairal

We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm.

Image Denoising Stereo Matching

Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration

1 code implementation ECCV 2020 Bruno Lecouat, Jean Ponce, Julien Mairal

Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling.

Demosaicking Denoising

Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile

no code implementations ICLR 2019 Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras

Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions

1 code implementation9 Feb 2019 Yasin Yazici, Bruno Lecouat, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar

We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities.

Manifold regularization with GANs for semi-supervised learning

1 code implementation ICLR 2019 Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay Chandrasekhar

Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images.

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

no code implementations7 Jul 2018 Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras

Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.

Semi-Supervised Learning with GANs: Revisiting Manifold Regularization

2 code implementations23 May 2018 Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay R. Chandrasekhar

GANS are powerful generative models that are able to model the manifold of natural images.

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