Learning Phase

Research into learning phases in artificial neural networks (ANNs) focuses on characterizing the distinct stages of training, revealing how models acquire and utilize information. Current investigations utilize various architectures, including ResNets, VGGs, and transformers, to analyze learning dynamics through metrics like reconstruction loss and prediction accuracy, often identifying multiple phases such as initial fitting, compression, and sometimes a later "grokking" phase where generalization improves unexpectedly. These studies aim to improve understanding of model behavior, leading to better training strategies, such as optimized transfer learning techniques, and potentially shedding light on the fundamental processes of learning itself.