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Why is SAM Robust to Label Noise?
Sharpness-Aware Minimization (SAM) excels in label noise robustness, with peak performance under early stopping, attributed to changes in logit term and network Jacobian. Alternative methods mimic SAM's regularization effects effectively.
https://arxiv.org/abs//2405.03676
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
Sharpness-Aware Minimization (SAM) excels in label noise robustness, with peak performance under early stopping, attributed to changes in logit term and network Jacobian. Alternative methods mimic SAM's regularization effects effectively.
https://arxiv.org/abs//2405.03676
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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