DifCluE: Generating Counterfactual Explanations with Diffusion Autoencoders and modal clustering

• URL: http://arxiv.org/abs/2502.11509v1
• Date: Mon, 17 Feb 2025 07:17:37 GMT
• Title: DifCluE: Generating Counterfactual Explanations with Diffusion Autoencoders and modal clustering
• Authors: Suparshva Jain, Amit Sangroya, Lovekesh Vig,
• Abstract summary: 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. We introduce a novel methodology, DifCluE, which consistently identifies these modes and produces more reliable counterfactual explanations.
• Score: 11.161081261781659
• License:
• Abstract: 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 advance- ment in model interpretability.

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