Download compressed checkpoints from the table below, put them under the output folder, and accordingly modify the --pretrained of the scripts. For example, to evaluate a 2x compressed model: python ...

Abstract: Communication-efficient federated learning benefits from neural network pruning, as it speeds up training and reduces model size. However, existing pruning techniques may not be optimally ...

Abstract: Filter pruning has gained widespread adoption for the purpose of compressing and speeding up convolutional neural networks (CNNs). However, the existing approaches are still far from ...