With the development of computational power and techniques for data
collection, deep learning demonstrates a superior performance over most of
existing algorithms on benchmark data sets. Many efforts have been devoted to
studying the mechanism of deep learning. One important observation is that deep
learning can learn the discriminative patterns from raw materials directly in a
task-dependent manner. Therefore, the representations obtained by deep learning
outperform hand-crafted features significantly. However, those patterns are
often learned from super-class labels due to a limited availability of
fine-grained labels, while fine-grained patterns are desired in many real-world
applications such as visual search in online shopping. To mitigate the
challenge, we propose an algorithm to learn the fine-grained patterns
sufficiently when only super-class labels are available. The effectiveness of
our method can be guaranteed with the theoretical analysis. Extensive
experiments on real-world data sets demonstrate that the proposed method can
significantly improve the performance on target tasks corresponding to
fine-grained classes, when only super-class information is available for
training
