Metric Learning Loss

Metric learning loss functions aim to learn embedding spaces where semantically similar data points are clustered closely together, while dissimilar points are separated. Current research focuses on developing novel loss functions that improve efficiency, accuracy, and robustness, particularly within Siamese networks and for multimodal data, often employing techniques like triplet loss, proxy losses, and Wasserstein distance. These advancements are impacting various fields, including image retrieval, object detection, and medical image analysis, by enabling more accurate and efficient similarity comparisons and improved model performance in challenging scenarios like few-shot learning and out-of-distribution detection.