Contrastive Loss

Contrastive loss is a machine learning technique that improves model performance by learning representations that maximize the similarity between similar data points (e.g., images of the same object) while minimizing similarity between dissimilar points. Current research focuses on refining contrastive loss functions, often incorporating additional constraints or integrating them with other learning paradigms like self-supervised learning and semi-supervised learning, and applying them to various architectures including transformers and autoencoders. This approach has proven effective across diverse applications, including image classification, speaker verification, and graph anomaly detection, leading to improved accuracy and robustness in various machine learning tasks.