  With the development of computational power and techniques for data
collection, deep learning demonstrates a superior performance over most
existing algorithms on visual 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, for some
real-world applications, it is too expensive to collect the task-specific
labels, such as visual search in online shopping. Compared to the limited
availability of these task-specific labels, their coarse-class labels are much
more affordable, but representations learned from them can be suboptimal for
the target task. To mitigate this challenge, we propose an algorithm to learn
the fine-grained patterns for the target task, when only its coarse-class
labels are available. More importantly, we provide a theoretical guarantee for
this. Extensive experiments on real-world data sets demonstrate that the
proposed method can significantly improve the performance of learned
representations on the target task, when only coarse-class information is
available for training. Code is available at
\url{https://github.com/idstcv/CoIns}.
