Proxy Based Loss

Proxy-based loss functions are a key component of deep metric learning, aiming to efficiently learn effective embeddings by comparing data points to representative "proxies" instead of all pairwise comparisons. Current research focuses on improving proxy optimization strategies, including incorporating relational structures within embeddings, regularization techniques to enhance out-of-distribution generalization, and dynamically weighting losses based on data importance. These advancements lead to faster convergence, improved accuracy in various applications like keyword spotting and image retrieval, and more robust performance under noisy or shifting data distributions.