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- W2963173680 abstract "Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing. However, existing methods have limited scalability. In this paper, instead of convolving with a dictionary shared by all samples, we propose the use of a sample-dependent dictionary in which filters are obtained as linear combinations of a small set of base filters learned from the data. This added flexibility allows a large number of sample-dependent patterns to be captured, while the resultant model can still be efficiently learned by online learning. Extensive experimental results show that the proposed method outperforms existing CSC algorithms with significantly reduced time and space requirements." @default.
- W2963173680 created "2019-07-30" @default.
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- W2963173680 date "2018-04-27" @default.
- W2963173680 modified "2023-09-27" @default.
- W2963173680 title "Online Convolutional Sparse Coding with Sample-Dependent Dictionary" @default.
- W2963173680 doi "https://doi.org/10.48550/arxiv.1804.10366" @default.
- W2963173680 hasPublicationYear "2018" @default.
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