output
20062026
most citedA review: Deep learning for medical image segmentation using multi-modality fusion

677 citations

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39 papers · 1 filter

cs.LG2025★ 2 cited

Universal Domain Adaptation Benchmark for Time Series Data Representation

Romain Mussard, Fannia Pacheco, Maxime Berar +2

Deep learning models have significantly improved the ability to detect novelties in time series (TS) data. This success is attributed to their strong representation capabilities. H…

cs.LG2025

PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection

Samy-Melwan Vilhes, Gilles Gasso, Mokhtar Z Alaya

Time series anomaly detection (TSAD) focuses on identifying whether observations in streaming data deviate significantly from normal patterns. With the prevalence of connected devi…

cs.LG2025

Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Series

Romain Mussard, Fannia Pacheco, Maxime Berar +2

Universal Domain Adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain, even when their classes are not fully shared. Few dedicat…

cs.LG2025

Mamba base PKD for efficient knowledge compression

José Medina, Amnir Hadachi, Paul Honeine +1

Deep neural networks (DNNs) have remarkably succeeded in various image processing tasks. However, their large size and computational complexity present significant challenges for d…

cs.LG2024

Gaussian-Smoothed Sliced Probability Divergences

Mokhtar Z. Alaya, Alain Rakotomamonjy, Maxime Berar +1

Gaussian smoothed sliced Wasserstein distance has been recently introduced for comparing probability distributions, while preserving privacy on the data. It has been shown that it…

cs.LG2023

Maximal Independent Sets for Pooling in Graph Neural Networks

Stevan Stanovic, Benoit Gaüzère, Luc Brun

Convolutional Neural Networks (CNNs) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discre…