  Due to the existence of label noise in web images and the high memorization
capacity of deep neural networks, training deep fine-grained (FG) models
directly through web images tends to have an inferior recognition ability. In
the literature, to alleviate this issue, loss correction methods try to
estimate the noise transition matrix, but the inevitable false correction would
cause severe accumulated errors. Sample selection methods identify clean
("easy") samples based on the fact that small losses can alleviate the
accumulated errors. However, "hard" and mislabeled examples that can both boost
the robustness of FG models are also dropped. To this end, we propose a
certainty-based reusable sample selection and correction approach, termed as
CRSSC, 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 then
leverage them together with clean examples to update the networks. We
demonstrate the superiority of the proposed approach from both theoretical and
experimental perspectives.
