Abstract: Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks. However, the parameter-grouping patterns vary widely across different models, ...
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 ...
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