  Labeling objects at a subordinate level typically requires expert knowledge,
which is not always available when using random annotators. As such, learning
directly from web images for fine-grained recognition has attracted broad
attention. However, the presence of label noise and hard examples in web images
are two obstacles for training robust fine-grained recognition models.
Therefore, in this paper, we propose a novel approach for removing irrelevant
samples from real-world web images during training, while employing useful hard
examples to update the network. Thus, our approach can alleviate the harmful
effects of irrelevant noisy web images and hard examples to achieve better
performance. Extensive experiments on three commonly used fine-grained datasets
demonstrate that our approach is far superior to current state-of-the-art
web-supervised methods.
