Representation Matching

Representation matching focuses on aligning the feature representations learned by different neural networks, aiming to improve generalization, knowledge transfer, and robustness. Current research explores various techniques, including gradient and representation alignment, often within a student-teacher framework where a robust or high-performing model's features guide the training of a new model. This approach shows promise in accelerating training, enhancing performance on challenging datasets, and transferring desirable properties like adversarial robustness, thereby impacting both model efficiency and security in machine learning applications.