Discriminative Model

Discriminative models are machine learning models designed to directly learn the mapping between input data and desired outputs, primarily focusing on accurate prediction. Current research emphasizes improving their robustness and generalization capabilities, particularly in handling out-of-distribution data and noisy labels, often through hybrid approaches combining discriminative models with generative models or incorporating techniques like diffusion models and contrastive learning. This research is significant because it addresses limitations of purely discriminative approaches, leading to more reliable and adaptable models across diverse applications, including speech enhancement, image classification, and natural language processing.