  In this paper, we showed that the feature map of a convolution layer is
equivalent to the unnormalized log posterior of a special kind of Gaussian
mixture for image modeling. Then we expanded the model to drive diverse
features and proposed a corresponding EM algorithm to learn the model. Learning
convolution weights using this approach is efficient, guaranteed to converge,
and does not need supervised information. Code is available at:
https://github.com/LifanLiang/CALM.
