Counterfactual Image

Counterfactual image generation focuses on creating modified images that answer "what if" questions about image features and their impact on model predictions. Current research emphasizes using diffusion models, generative adversarial networks (GANs), and variational autoencoders to generate these counterfactuals, often incorporating techniques like contrastive learning and causal inference to improve realism and interpretability. This field is significant for enhancing the explainability of black-box models, particularly in medical imaging, where understanding model decisions is crucial for trust and clinical adoption, and for mitigating biases in vision-language models.