Related papers: SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

URL: http://arxiv.org/abs/2012.00925v1
Date: Wed, 2 Dec 2020 01:49:47 GMT
Title: SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
Authors: Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long
Abstract summary: SemiNLL is a versatile framework that combines SS strategies and SSL models in an end-to-end manner. Our framework can absorb various SS strategies and SSL backbones, utilizing their power to achieve promising performance.
Score: 58.26384597768118
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Deep learning with noisy labels is a challenging task. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL) model achieved state-of-the-art performance. Intuitively, better performance could be achieved if stronger SS strategies and SSL models are employed. Following this intuition, one might easily derive various effective noisy-label learning methods using different combinations of SS strategies and SSL models, which is, however, reinventing the wheel in essence. To prevent this problem, we propose SemiNLL, a versatile framework that combines SS strategies and SSL models in an end-to-end manner. Our framework can absorb various SS strategies and SSL backbones, utilizing their power to achieve promising performance. We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.

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