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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2019.2914376
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
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|a Mahdizadehaghdam, Shahin
|e verfasserin
|4 aut
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| 245 |
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|a Deep Dictionary Learning
|b A PARametric NETwork Approach
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|c 2019
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method for learning a hierarchy of synthesis dictionaries with an image classification goal. The dictionaries and classification parameters are trained by a classification objective, and the sparse features are extracted by reducing a reconstruction loss in each layer. The reconstruction objectives in some sense regularize the classification problem and inject source signal information in the extracted features. The performance of the proposed hierarchical method increases by adding more layers, which consequently makes this model easier to tune and adapt. The proposed algorithm furthermore, shows remarkably lower fooling rate in presence of adversarial perturbation. The validation of the proposed approach is based on its classification performance using four benchmark datasets and is compared to a CNN of similar size
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|a Journal Article
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|a Panahi, Ashkan
|e verfasserin
|4 aut
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| 700 |
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|a Krim, Hamid
|e verfasserin
|4 aut
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| 700 |
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|a Dai, Liyi
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2019) vom: 07. Mai
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|x 1941-0042
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|g year:2019
|g day:07
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|u http://dx.doi.org/10.1109/TIP.2019.2914376
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