Contrastive Model

Contrastive learning is a self-supervised machine learning approach that learns representations by comparing and contrasting data points, aiming to create embeddings that cluster similar data and separate dissimilar data. Current research focuses on improving contrastive models across various modalities (vision, language, audio) through techniques like prompt tuning, in-context learning, and adversarial methods, often leveraging pretrained models for efficiency. These advancements are impacting diverse fields, enhancing performance in tasks such as zero-shot classification, retrieval, and improving the safety and robustness of large language models and federated learning systems.