Paper
Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis
Authors
Chen Feng, Zhuo Zhi, Zhao Huang, Jiawei Ge, Ling Xiao, Nicu Sebe, Georgios Tzimiropoulos, Ioannis Patras
Abstract
Statistically consistent methods based on the noise transition matrix ($T$) offer a theoretically grounded solution to Learning with Noisy Labels (LNL), with guarantees of convergence to the optimal clean-data classifier. In practice, however, these methods are often outperformed by empirical approaches such as sample selection, and this gap is usually attributed to the difficulty of accurately estimating $T$. The common assumption is that, given a perfect $T$, noise-correction methods would recover their theoretical advantage. In this work, we put this longstanding hypothesis to a decisive test. We conduct experiments under idealized conditions, providing correction methods with a perfect, oracle transition matrix. Even under these ideal conditions, we observe that these methods still suffer from performance collapse during training. This compellingly demonstrates that the failure is not fundamentally a $T$-estimation problem, but stems from a more deeply rooted flaw. To explain this behaviour, we provide a unified analysis that links three levels: macroscopic convergence states, microscopic optimisation dynamics, and information-theoretic limits on what can be learned from noisy labels. Together, these results give a formal account of why ideal noise correction fails and offer concrete guidance for designing more reliable methods for learning with noisy labels.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.12997v1</id>\n <title>Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis</title>\n <updated>2026-03-13T13:53:04Z</updated>\n <link href='https://arxiv.org/abs/2603.12997v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.12997v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Statistically consistent methods based on the noise transition matrix ($T$) offer a theoretically grounded solution to Learning with Noisy Labels (LNL), with guarantees of convergence to the optimal clean-data classifier. In practice, however, these methods are often outperformed by empirical approaches such as sample selection, and this gap is usually attributed to the difficulty of accurately estimating $T$. The common assumption is that, given a perfect $T$, noise-correction methods would recover their theoretical advantage. In this work, we put this longstanding hypothesis to a decisive test. We conduct experiments under idealized conditions, providing correction methods with a perfect, oracle transition matrix. Even under these ideal conditions, we observe that these methods still suffer from performance collapse during training. This compellingly demonstrates that the failure is not fundamentally a $T$-estimation problem, but stems from a more deeply rooted flaw. To explain this behaviour, we provide a unified analysis that links three levels: macroscopic convergence states, microscopic optimisation dynamics, and information-theoretic limits on what can be learned from noisy labels. Together, these results give a formal account of why ideal noise correction fails and offer concrete guidance for designing more reliable methods for learning with noisy labels.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-13T13:53:04Z</published>\n <arxiv:comment>Accepted to CVPR2026</arxiv:comment>\n <arxiv:primary_category term='cs.LG'/>\n <author>\n <name>Chen Feng</name>\n </author>\n <author>\n <name>Zhuo Zhi</name>\n </author>\n <author>\n <name>Zhao Huang</name>\n </author>\n <author>\n <name>Jiawei Ge</name>\n </author>\n <author>\n <name>Ling Xiao</name>\n </author>\n <author>\n <name>Nicu Sebe</name>\n </author>\n <author>\n <name>Georgios Tzimiropoulos</name>\n </author>\n <author>\n <name>Ioannis Patras</name>\n </author>\n </entry>"
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