Generative Classifier

Generative classifiers leverage the power of generative models to improve classification accuracy and robustness, addressing limitations of traditional discriminative approaches. Current research focuses on developing novel generative classifier architectures, such as those based on diffusion models, variational autoencoders, and Gaussian mixture models, and exploring their application in diverse fields like medical image analysis and natural language processing. This approach offers advantages in handling imbalanced datasets, improving uncertainty quantification, and enhancing model interpretability, leading to more reliable and insightful classification results across various applications.