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cs.LG2025
Multi-Label Contrastive Learning : A Comprehensive Study
Alexandre Audibert, Aurélien Gauffre, Massih-Reza Amini
Multi-label classification, which involves assigning multiple labels to a single input, has emerged as a key area in both research and industry due to its wide-ranging applications…
cs.LG2024★ 2 cited
A Unified Contrastive Loss for Self-Training
Aurelien Gauffre, Julien Horvat, Massih-Reza Amini
Self-training methods have proven to be effective in exploiting abundant unlabeled data in semi-supervised learning, particularly when labeled data is scarce. While many of these a…