Study of Coarse-to-Fine Class Activation Mapping Algorithms Based on Contrastive Layer-wise Relevance Propagation

SUN Hui; SHI Yulong; WANG Rui

doi:10.11999/JEIT220113

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1454-1463

SUN Hui, SHI Yulong, WANG Rui. Study of Coarse-to-Fine Class Activation Mapping Algorithms Based on Contrastive Layer-wise Relevance Propagation[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1454-1463. doi: 10.11999/JEIT220113

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SUN Hui, SHI Yulong, WANG Rui. Study of Coarse-to-Fine Class Activation Mapping Algorithms Based on Contrastive Layer-wise Relevance Propagation[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1454-1463. doi: 10.11999/JEIT220113

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Study of Coarse-to-Fine Class Activation Mapping Algorithms Based on Contrastive Layer-wise Relevance Propagation

doi: 10.11999/JEIT220113

College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

Funds: The Natural Science Foundation of Tianjin (18JCYBJC42300)

Received Date: 2022-01-27
Accepted Date: 2022-06-17
Rev Recd Date: 2022-06-10

Available Online: 2022-06-20

Publish Date: 2023-04-10

Abstract

Abstract

Deep learning algorithms represented by Convolutional Neural Networks (CNN) are highly dependent on the nonlinearity of the model and debugging techniques, which have generally black-box properties during practical applications, limiting severely their further development in security-sensitive fields. To this end, a Coarse-to-Fine Class Activation Mapping (CF-CAM) algorithm is proposed for diagnosing the decision-making behaviors of deep neural networks. The algorithm re-establishes the relationship between the feature map and the model decision, uses the contrastive layer-wise relevance propagation theory to obtain the contribution of each position in the feature map to the network decision, generates a spatial-level correlation mask and finds the important area that affects the model decision. After that, the mask is linearly weighted with the fuzzed input image and re-input into the network to obtain the target score of the feature map, and the deep neural network is explained from the coarse stage to the fine stage in the spatial domain and the channel domain. The experimental results show that the CF-CAM proposed in this paper has obvious advantages in terms of faithfulness and localization performance compared to other methods. In addition, this paper applies CF-CAM as a data enhancement strategy for the task of fine-grained classification of birds, which can effectively improve the accuracy of network recognition by learning difficult samples, further verify the effectiveness and superiority of this method.

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References(31)

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