Layer Wise Distillation

Layer-wise distillation is a model compression technique aiming to create smaller, faster neural networks ("student" models) that retain the accuracy of larger, more computationally expensive "teacher" models. Current research focuses on applying this method to diverse architectures, including transformers, conformers, and even spiking neural networks, often incorporating techniques like structured pruning and adaptive distillation strategies to optimize both speed and accuracy. This approach is significant because it enables the deployment of powerful deep learning models on resource-constrained devices, impacting fields ranging from natural language processing and computer vision to medical image analysis and music generation.