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PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
May 1, 2024, 4:46 a.m. | Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro
cs.CV updates on arXiv.org arxiv.org
Abstract: The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with noisy-label (LNL) techniques that focus on separating noisy- and clean-label samples to apply different learning strategies to each group of samples. Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples, yet our empirical observations reveal their limitations, especially for labels with instance dependent …
abstract agreement apply arxiv challenge cs.cv deep learning effects emergence focus labels overfitting peer sample samples strategies training type
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