Concept-based Visual Counterfactual Explanations with Diffusion Models
Quick Answer
C-VCE introduces a novel diffusion framework for visual counterfactual explanations, integrating a concept bottleneck layer to enhance interpretability and robustness.
Quick Take
This method achieves improved flip rates on CelebA while producing less distorted counterfactuals compared to traditional models reliant on external classifiers, making it a practical tool for vision systems in critical applications.
Key Points
- C-VCE integrates a classifier directly into the generative model for better robustness.
- Users can toggle semantic concepts during sampling to control image modifications.
- The model achieves improved flip rates on CelebA while maintaining visual fidelity.
- A probabilistic regularizer ensures minimal and controlled edits to relevant image regions.
- C-VCE is designed for safety-critical domains, reducing reliance on external classifiers.
Paper Resources
Source Excerpt
Visual counterfactual explanations aim to answer "what minimal change to this image would flip the model's prediction? ", and are increasingly important as vision models are deployed in safety-critical domains (e. g. , medicine). Existing diffusion-based methods can produce realistic edits, but they rely on external classifiers that must work reliably on noisy images, which makes them fragile and hard to deploy for robust explanations. We introduce C-VCE, a new diffusion framework that builds the c
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
AINTMA, an autonomous test management architecture utilizing six specialized AI agents, achieves 88.4% test prioritization accuracy and reduces defect escape rates from 8.3% to 2.1%. The system demonstrates a 340% ROI within nine months, showcasing the potential of agentic AI in enhancing software quality management in cloud environments.