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Due to the existence of label noise in web images and the high memorizationcapacity of deep neural networks, training deep fine-grained (FG) modelsdirectly through web images tends to have an inferior recognition ability. Inthe literature, to alleviate this issue, loss correction methods try toestimate the noise transition matrix, but the inevitable false correction wouldcause severe accumulated errors. Sample selection methods identify clean("easy") samples based on the fact that small losses can alleviate theaccumulated errors. However, "hard" and mislabeled examples that can both boostthe robustness of FG models are also dropped. To this end, we propose acertainty-based reusable sample selection and correction approach, termed asCRSSC, for coping with label noise in training deep FG models with web images.Our key idea is to additionally identify and correct reusable samples, and thenleverage them together with clean examples to update the networks. Wedemonstrate the superiority of the proposed approach from both theoretical andexperimental perspectives.

Salvage Reusable Samples from Noisy Data for Robust Learning