Decoupled Training

Decoupled training is a machine learning paradigm that separates the learning of feature representations from the training of the classifier, aiming to improve model performance and generalization, particularly in challenging scenarios like long-tailed classification and multi-domain learning. Current research focuses on optimizing both stages independently, exploring techniques like logits retargeting, stochastic weight averaging, and the development of specialized architectures such as multi-headed models or separate structure and texture generators for improved efficiency. This approach shows promise in enhancing model robustness, reducing computational costs, and improving generalization across diverse datasets and tasks, impacting fields ranging from image recognition to robotics.