ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural Faithfulness in SpArX
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909525616885760 |
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| author | Kim, Ungsik |
| author_facet | Kim, Ungsik |
| contents | In the field of Explainable Artificial Intelligence (XAI), argumentative XAI approaches have been proposed to represent the internal reasoning process of deep neural networks in a more transparent way by interpreting hidden nodes as arguements. However, as the number of layers increases, existing compression methods simplify all layers at once, which lead to high accumulative information loss. To compensate for this, we propose an iterative layer-by-layer compression technique in which each layer is compressed separately and the reduction error in the next layer is immediately compensated for, thereby improving the overall input-output and structural fidelity of the model. Experiments on the Breast Cancer Diagnosis dataset show that, compared to traditional compression, the method reduces input-output and structural unfaithfulness, and maintains a more consistent attack-support relationship in the Argumentative Explanation scheme. This is significant because it provides a new way to make complex MLP models more compact while still conveying their internal inference logic without distortion. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_03693 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural Faithfulness in SpArX Kim, Ungsik Artificial Intelligence In the field of Explainable Artificial Intelligence (XAI), argumentative XAI approaches have been proposed to represent the internal reasoning process of deep neural networks in a more transparent way by interpreting hidden nodes as arguements. However, as the number of layers increases, existing compression methods simplify all layers at once, which lead to high accumulative information loss. To compensate for this, we propose an iterative layer-by-layer compression technique in which each layer is compressed separately and the reduction error in the next layer is immediately compensated for, thereby improving the overall input-output and structural fidelity of the model. Experiments on the Breast Cancer Diagnosis dataset show that, compared to traditional compression, the method reduces input-output and structural unfaithfulness, and maintains a more consistent attack-support relationship in the Argumentative Explanation scheme. This is significant because it provides a new way to make complex MLP models more compact while still conveying their internal inference logic without distortion. |
| title | ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural Faithfulness in SpArX |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.03693 |