  While sparse coding-based clustering methods have shown to be successful,
their bottlenecks in both efficiency and scalability limit the practical usage.
In recent years, deep learning has been proved to be a highly effective,
efficient and scalable feature learning tool. In this paper, we propose to
emulate the sparse coding-based clustering pipeline in the context of deep
learning, leading to a carefully crafted deep model benefiting from both. A
feed-forward network structure, named TAGnet, is constructed based on a
graph-regularized sparse coding algorithm. It is then trained with
task-specific loss functions from end to end. We discover that connecting deep
learning to sparse coding benefits not only the model performance, but also its
initialization and interpretation. Moreover, by introducing auxiliary
clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet
and obtain a further performance boost. Extensive experiments demonstrate that
the proposed model gains remarkable margins over several state-of-the-art
methods.
