期刊
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
卷 -, 期 -, 页码 2736-2740出版社
IEEE
关键词
convolutional sparse coding; dictionary learning; sparse representation; supervised learning; visual recognition
Convolutional sparse coding (CSC) has become an important method in image processing and computer vision. In this paper we focus on visual recognition problems and apply CSC as a feature learning method. We propose a task-specific approach to treat the dictionary of CSC as parameters for a larger learning framework. These parameters are differentiable under mild conditions, and could be updated end-to-end using back-propagation when the errors from the task objectives are provided. We perform several experiments to show that such method provides a more discriminate representation compared with previous CSC methods, and this data driven approach is effective for visual recognition problems.
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