C-VCE: Diffusion Models for Concept-Based Visual Counterfactual Explanations
A new diffusion framework known as C-VCE has been developed by researchers to create visual counterfactual explanations. This innovative model integrates a classifier into the generative framework through a concept bottleneck layer. Users can adjust semantic concepts during the sampling process, allowing for minimal alterations to specific image areas while keeping the remainder intact. In contrast to current diffusion methods that depend on external classifiers analyzing noisy images—leading to fragility—C-VCE utilizes human-understandable features to direct modifications. This framework maintains feature relationships and generates realistic edits without needing a separate noise-resistant classifier. This advancement meets the increasing demand for reliable explanations in critical fields such as medicine, where visual models are gaining traction. The paper can be found on arXiv, reference 2607.22544.
Key facts
- C-VCE is a diffusion framework for visual counterfactual explanations.
- It builds the classifier into the generative model via a concept bottleneck layer.
- Users can toggle on/off semantic concepts during sampling.
- Edits are minimal and preserve the rest of the image.
- It respects feature correlations.
- Existing methods rely on external classifiers that work on noisy images.
- C-VCE uses human-interpretable features (concepts) instead of pixel-level edits.
- The approach is relevant for safety-critical domains like medicine.
Entities
Institutions
- arXiv