4 citations · 5 across the 6 of their papers we have counts for
10 papers
Can neural networks extrapolate? Discussion of a theorem by Pedro Domingos
Adrien Courtois, Jean-Michel Morel, Pablo Arias
Neural networks trained on large datasets by minimizing a loss have become the state-of-the-art approach for resolving data science problems, particularly in computer vision, image…
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites
Ngoc Long Nguyen, Jérémy Anger, Axel Davy +2
Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means. In this work, we propose a super-resoluti…
Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?
Valéry Dewil, Aranud Barral, Gabriele Facciolo +1
Supervised training has led to state-of-the-art results in image and video denoising. However, its application to real data is limited since it requires large datasets of noisy-cle…
Investigating Neural Architectures by Synthetic Dataset Design
Adrien Courtois, Jean-Michel Morel, Pablo Arias
Recent years have seen the emergence of many new neural network structures (architectures and layers). To solve a given task, a network requires a certain set of abilities reflecte…
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution
Ngoc Long Nguyen, Jérémy Anger, Axel Davy +2
The PROBA-V Super-Resolution challenge distributes real low-resolution image series and corresponding high-resolution targets to advance research on Multi-Image Super Resolution (M…
Self-Supervised training for blind multi-frame video denoising
Valéry Dewil, Jérémy Anger, Axel Davy +3
We propose a self-supervised approach for training multi-frame video denoising networks. These networks predict frame t from a window of frames around t. Our self-supervised approa…