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Adaptive sparse coding methods learn a possibly overcomplete set of basisfunctions, such that natural image patches can be reconstructed by linearlycombining a small subset of these bases. The applicability of these methods tovisual object recognition tasks has been limited because of the prohibitivecost of the optimization algorithms required to compute the sparserepresentation. In this work we propose a simple and efficient algorithm tolearn basis functions. After training, this model also provides a fast andsmooth approximator to the optimal representation, achieving even betteraccuracy than exact sparse coding algorithms on visual object recognitiontasks.

Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition