Contrastive Objective

Contrastive learning aims to learn robust representations by maximizing the similarity between different augmented views of the same data point while minimizing similarity between different data points. Current research focuses on improving contrastive objectives through techniques like curriculum learning, handling noisy views, and incorporating additional information such as knowledge graphs or expert knowledge to guide the augmentation process. This approach is proving valuable across diverse fields, enhancing performance in recommendation systems, speech translation, fault diagnosis, and various other machine learning tasks by improving representation learning in data-scarce or noisy environments.