2024
Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation
ICLR 2024poster
The conventional deep learning paradigm often involves training a deep model on a server and then deploying the model or its distilled ones to resource-limited edge devices. Usually, the models shall remain fixed once deployed (at least for some period) due to the potential high cost of model adapta…