Center Loss

Center loss is a machine learning technique aimed at improving the discriminative power of learned feature embeddings by encouraging data points of the same class to cluster tightly around a class-specific center. Current research focuses on integrating center loss with various model architectures, including transformers and convolutional neural networks, often in conjunction with other loss functions like contrastive loss, to address challenges such as long-tailed distributions, domain adaptation, and cross-modal inconsistencies. This approach enhances the performance of tasks like object detection, person re-identification, and relation extraction, particularly in scenarios with imbalanced or noisy data, leading to more robust and accurate models in diverse applications.