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cs.LG2022★ 4 cited
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels
Daehwan Kim, Kwangrok Ryoo, Hansang Cho +1
Annotating the dataset with high-quality labels is crucial for performance of deep network, but in real world scenarios, the labels are often contaminated by noise. To address this…
cs.LG2022
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee +5
Semi-supervised learning (SSL) has recently proven to be an effective paradigm for leveraging a huge amount of unlabeled data while mitigating the reliance on large labeled data. C…