  Deep subspace clustering has attracted increasing attention in recent years.
Almost all the existing works are required to load the whole training data into
one batch for learning the self-expressive coefficients in the framework of
deep learning. Although these methods achieve promising results, such a
learning fashion severely prevents from the usage of deeper neural network
architectures (e.g., ResNet), leading to the limited representation abilities
of the models. In this paper, we propose a new deep subspace clustering
framework, motivated by the energy-based models. In contrast to previous
approaches taking the weights of a fully connected layer as the self-expressive
coefficients, we propose to learn an energy-based network to obtain the
self-expressive coefficients by mini-batch training. By this means, it is no
longer necessary to load all data into one batch for learning, and it thus
becomes a reality that we can utilize deeper neural network models for subspace
clustering. Considering the powerful representation ability of the recently
popular self-supervised learning, we attempt to leverage self-supervised
representation learning to learn the dictionary. Finally, we propose a joint
framework to learn both the self-expressive coefficients and dictionary
simultaneously, and train the model in an end-to-end manner. The experiments
are performed on three publicly available datasets, and extensive experimental
results demonstrate our method can significantly outperform the other related
approaches. For instance, on the three datasets, our method can averagely
achieve $13.8\%$, $15.4\%$, $20.8\%$ improvements in terms of Accuracy, NMI,
and ARI over SENet which is proposed very recently and obtains the second best
results in the experiments.
