We introduce an explainable generative model by applying sparse operation on
the feature maps of the generator network. Meaningful hierarchical
representations are obtained using the proposed generative model with sparse
activations. The convolutional kernels from the bottom layer to the top layer
of the generator network can learn primitives such as edges and colors, object
parts, and whole objects layer by layer. From the perspective of the generator
network, we propose a method for inducing both sparse coding and the AND-OR
grammar for images. Experiments show that our method is capable of learning
meaningful and explainable hierarchical representations.

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