Diffusion Classifier

Diffusion classifiers leverage the density estimation capabilities of diffusion models for classification tasks, offering a generative alternative to traditional discriminative methods. Current research focuses on improving their robustness to adversarial attacks, enhancing their efficiency for real-time applications like avatar driving, and exploring their use in continual learning and zero-shot scenarios, often employing class-conditional models and Bayesian principles. This approach shows promise in various fields, including medical image analysis and 3D object recognition, by offering advantages in anomaly detection, data efficiency, and interpretability compared to existing techniques. The ability to perform zero-shot classification and achieve certified robustness highlights the significant potential of diffusion classifiers.