  Constructing fine-grained image datasets typically requires domain-specific
expert knowledge, which is not always available for crowd-sourcing platform
annotators. Accordingly, learning directly from web images becomes an
alternative method for fine-grained visual recognition. However, label noise in
the web training set can severely degrade the model performance. To this end,
we propose a data-driven meta-set based approach to deal with noisy web images
for fine-grained recognition. Specifically, guided by a small amount of clean
meta-set, we train a selection net in a meta-learning manner to distinguish in-
and out-of-distribution noisy images. To further boost the robustness of model,
we also learn a labeling net to correct the labels of in-distribution noisy
data. In this way, our proposed method can alleviate the harmful effects caused
by out-of-distribution noise and properly exploit the in-distribution noisy
samples for training. Extensive experiments on three commonly used fine-grained
datasets demonstrate that our approach is much superior to state-of-the-art
noise-robust methods.
