Bruno Lecouat

According to our database1, Bruno Lecouat authored at least 18 papers between 2018 and 2023.

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Bibliography

2023
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation.
CoRR, 2023

2022
High dynamic range and super-resolution from raw image bursts.
ACM Trans. Graph., 2022

Mixed Membership Generative Adversarial Networks.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

2021
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results.
CoRR, 2021

Aliasing is your Ally: End-to-End Super-Resolution from Raw Image Bursts.
CoRR, 2021

Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Designing and Learning Trainable Priors with Non-Cooperative Games.
CoRR, 2020

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fully Trainable and Interpretable Non-local Sparse Models for Image Restoration.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
Revisiting Non Local Sparse Models for Image Restoration.
CoRR, 2019

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions.
CoRR, 2019

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images.
CoRR, 2018

Manifold regularization with GANs for semi-supervised learning.
CoRR, 2018

Mirror descent in saddle-point problems: Going the extra (gradient) mile.
CoRR, 2018

Efficient GAN-Based Anomaly Detection.
CoRR, 2018

Semi-Supervised Learning With GANs: Revisiting Manifold Regularization.
Proceedings of the 6th International Conference on Learning Representations, 2018

Adversarially Learned Anomaly Detection.
Proceedings of the IEEE International Conference on Data Mining, 2018


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