AI Research
Concept-based Visual Counterfactual Explanations with Diffusion Models
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 realisti...
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ArXiv CS.AI
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ArXiv CS.AI
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This research introduces a novel diffusion model framework for visual counterfactual explanations by integrating a concept bottleneck layer. This approach enhances reliability by avoiding external classifiers, making AI decision-making more transparent and interpretable in critical applications.
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