Visual Counterfactual Explanation

Visual counterfactual explanation (VCE) aims to understand the decision-making of image classifiers by generating minimally altered images that change the classifier's prediction. Current research focuses on improving the realism and semantic consistency of these altered images, employing generative models like diffusion models and variational autoencoders, often incorporating techniques from adversarial attacks and robust learning. This work is significant for enhancing the transparency and trustworthiness of AI systems, particularly in high-stakes applications where understanding model decisions is crucial. Improved VCE methods are leading to better evaluation frameworks and a deeper understanding of classifier biases and vulnerabilities.