Layer Wise

Layer-wise training methods in deep learning focus on optimizing neural networks by training or adapting individual layers or blocks of layers sequentially or in parallel, rather than optimizing the entire network end-to-end. Current research explores layer-wise approaches within various architectures, including transformers and convolutional neural networks, to improve efficiency, address resource constraints (especially in federated learning and edge computing), enhance model interpretability, and mitigate issues like catastrophic forgetting and overfitting. These techniques offer significant potential for advancing both the theoretical understanding of deep learning and its practical applications by enabling training of larger models on resource-limited devices and improving model robustness and generalization.