activity
20182021
most citedSemi-Supervised Deep Learning for Abnormality Classification in Retinal Images

30 citations · 32 across the 3 of their papers we have counts for

collaborators

11 papers

cs.CV20211 cited

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

Goutam Bhat, Martin Danelljan, Radu Timofte +25

This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times hig…

cs.CV2021

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

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…

cs.CV2020

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

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 wh…

cs.CV2019

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

Bruno Lecouat, Jean Ponce, Julien Mairal

Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling. We propose a novel differentiable relaxation of joint sparsity that…

cs.LG20191 cited

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions

Yasin Yazıcı, Bruno Lecouat, Chuan-Sheng Foo +4

We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of $…

cs.CV201830 cited

Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

Bruno Lecouat, Ken Chang, Chuan-Sheng Foo +7

Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require…