Deep Metric Learning

Deep metric learning (DML) focuses on learning effective representations of data, embedding them into a space where distances accurately reflect semantic similarity. Current research emphasizes improving loss functions to address issues like embedding space collapse and threshold inconsistency, often incorporating techniques like proxy-based methods, triplet losses, and attention mechanisms within convolutional neural networks or transformers. These advancements enhance the accuracy and robustness of DML across various applications, including image retrieval, facial expression recognition, and even novel areas like encrypted video stream detection and human-robot interaction safety. The resulting improvements in similarity search and classification have significant implications for numerous fields.