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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.11509 |
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| _version_ | 1866916622420148224 |
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| author | Jain, Suparshva Sangroya, Amit Vig, Lovekesh |
| author_facet | Jain, Suparshva Sangroya, Amit Vig, Lovekesh |
| contents | Generating multiple counterfactual explanations for different modes within a class presents a significant challenge, as these modes are distinct yet converge under the same classification. Diffusion probabilistic models (DPMs) have demonstrated a strong ability to capture the underlying modes of data distributions. In this paper, we harness the power of a Diffusion Autoencoder to generate multiple distinct counterfactual explanations. By clustering in the latent space, we uncover the directions corresponding to the different modes within a class, enabling the generation of diverse and meaningful counterfactuals. We introduce a novel methodology, DifCluE, which consistently identifies these modes and produces more reliable counterfactual explanations. Our experimental results demonstrate that DifCluE outperforms the current state-of-the-art in generating multiple counterfactual explanations, offering a significant advancement in model interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DifCluE: Generating Counterfactual Explanations with Diffusion Autoencoders and modal clustering Jain, Suparshva Sangroya, Amit Vig, Lovekesh Machine Learning Artificial Intelligence Generating multiple counterfactual explanations for different modes within a class presents a significant challenge, as these modes are distinct yet converge under the same classification. Diffusion probabilistic models (DPMs) have demonstrated a strong ability to capture the underlying modes of data distributions. In this paper, we harness the power of a Diffusion Autoencoder to generate multiple distinct counterfactual explanations. By clustering in the latent space, we uncover the directions corresponding to the different modes within a class, enabling the generation of diverse and meaningful counterfactuals. We introduce a novel methodology, DifCluE, which consistently identifies these modes and produces more reliable counterfactual explanations. Our experimental results demonstrate that DifCluE outperforms the current state-of-the-art in generating multiple counterfactual explanations, offering a significant advancement in model interpretability. |
| title | DifCluE: Generating Counterfactual Explanations with Diffusion Autoencoders and modal clustering |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.11509 |