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cs.LG2025
Let the Void Be Void: Robust Open-Set Semi-Supervised Learning via Selective Non-Alignment
You Rim Choi, Subeom Park, Seojun Heo +2
Open-set semi-supervised learning (OSSL) leverages unlabeled data containing both in-distribution (ID) and unknown out-of-distribution (OOD) samples, aiming simultaneously to impro…
cs.LG2024
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation
Yujin Shin, Kichang Lee, Sungmin Lee +3
While federated learning leverages distributed client resources, it faces challenges due to heterogeneous client capabilities. This necessitates allocating models suited to clients…