基于扩散模型的无条件反事实解释生成方法

doi:10.16451/j.cnki.issn1003-6059.202411006

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Abstract Counterfactual explanations alter the model output by implementing minimal and interpretable modifications to input data, revealing key factors influencing model decisions. Existing counterfactual explanation methods based on diffusion models rely on conditional generation, requiring additional semantic information related to classification. However, ensuring semantic quality of the semantic information is challenging and computational costs are increased. To address these issues, an unconditional counterfactual explanation generation method based on the denoising diffusion implicit model(DDIM)is proposed. By leveraging the consistency exhibited by DDIM during the reverse denoising process, noisy images are treated as latent variables to control the generated outputs, thus making the diffusion model suitable for unconditional counterfactual explanation generation workflows. Then, the advantages of DDIM in filtering high-frequency noise and out-of-distribution perturbations are fully utilized, thereby reconstructing the unconditional counterfactual explanation workflow to generate semantically interpretable modifications. Extensive experiments on different datasets demonstrate that the proposed method achieves superior results across multiple metrics.

Key words： Deep Learning Interpretability Counterfactual Explanation Diffusion Model Adversarial Attack

Received: 12 September 2024

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61772284,62406148,62306339), Natural Science Foundation of Jiangsu Province(No.SBK2024047556)

Corresponding Authors: LI Yun, Ph.D., professor. His research interests include trusted artificial intelligence.

About author:: ZHONG Zhi, Master student. His research interests include machine learning.WANG Yu, Ph.D., lecturer. His research interests include machine learning and natural language processing.ZHU Ziye, Ph.D., lecturer. Her research interests include machine learning and natural language processing.

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Cite this article:

ZHONG Zhi,WANG Yu,ZHU Ziye等. Diffusion Models Based Unconditional Counterfactual Explanations Generation[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(11): 1010-1021.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202411006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I11/1010

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