Rethinking the Value of Labels for Instance-Dependent Label Noise Learning

• URL: http://arxiv.org/abs/2305.06247v2
• Date: Sun, 14 May 2023 04:14:48 GMT
• Title: Rethinking the Value of Labels for Instance-Dependent Label Noise Learning
• Authors: Hanwen Deng, Weijia Zhang, Min-Ling Zhang
• Abstract summary: noisy labels in real-world applications often depend on both the true label and the features. In this work, we tackle instance-dependent label noise with a novel deep generative model that avoids explicitly modeling the noise transition matrix. Our algorithm leverages casual representation learning and simultaneously identifies the high-level content and style latent factors from the data.
• Score: 43.481591776038144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Label noise widely exists in large-scale datasets and significantly degenerates the performances of deep learning algorithms. Due to the non-identifiability of the instance-dependent noise transition matrix, most existing algorithms address the problem by assuming the noisy label generation process to be independent of the instance features. Unfortunately, noisy labels in real-world applications often depend on both the true label and the features. In this work, we tackle instance-dependent label noise with a novel deep generative model that avoids explicitly modeling the noise transition matrix. Our algorithm leverages casual representation learning and simultaneously identifies the high-level content and style latent factors from the data. By exploiting the supervision information of noisy labels with structural causal models, our empirical evaluations on a wide range of synthetic and real-world instance-dependent label noise datasets demonstrate that the proposed algorithm significantly outperforms the state-of-the-art counterparts.

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