DifCluE: Generating Counterfactual Explanations with Diffusion Autoencoders and modal clustering

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.

@article{jain2025_2502.11509,
  title={ DifCluE: Generating Counterfactual Explanations with Diffusion Autoencoders and modal clustering },
  author={ Suparshva Jain and Amit Sangroya and Lovekesh Vig },
  journal={arXiv preprint arXiv:2502.11509},
  year={ 2025 }
}