64 citations · 358 across the 46 of their papers we have counts for
5 papers · 1 filter
Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network
Sangmin Bae, Sungnyun Kim, Jongwoo Ko +3
Contrastive loss has significantly improved performance in supervised classification tasks by using a multi-viewed framework that leverages augmentation and label information. The…
FedBABU: Towards Enhanced Representation for Federated Image Classification
Jaehoon Oh, Sangmook Kim, Se-Young Yun
Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a bles…
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
Gihun Lee, Minchan Jeong, Yongjin Shin +2
In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models. Although this precludes the need to access clients' data dir…
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Taehyeon Kim, Jaehoon Oh, NakYil Kim +2
Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.…
FINE Samples for Learning with Noisy Labels
Taehyeon Kim, Jongwoo Ko, Sangwook Cho +2
Modern deep neural networks (DNNs) become frail when the datasets contain noisy (incorrect) class labels. Robust techniques in the presence of noisy labels can be categorized into…