Successive Affine Learning

Successive affine learning (SAL) and its related methods, such as successive subspace learning (SSL), represent a novel approach to training deep neural networks (DNNs) by iteratively learning layers or subspaces. Current research focuses on improving efficiency and accuracy, particularly in resource-constrained environments like federated learning (FL), and on enhancing model interpretability. These techniques offer potential advantages in reducing computational complexity, improving performance with limited data, and enabling deployment on devices with varying capabilities, impacting fields such as medical image analysis and distributed machine learning.