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This paper emphasizes the significance to jointly exploit the problemstructure and the parameter structure, in the context of deep modeling. As aspecific and interesting example, we describe the deep double sparsity encoder(DDSE), which is inspired by the double sparsity model for dictionary learning.DDSE simultaneously sparsities the output features and the learned modelparameters, under one unified framework. In addition to its intuitive modelinterpretation, DDSE also possesses compact model size and low complexity.Extensive simulations compare DDSE with several carefully-designed baselines,and verify the consistently superior performance of DDSE. We further apply DDSEto the novel application domain of brain encoding, with promising preliminaryresults achieved.

Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But Also Parameters