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A Pytorch implementation for our ICCV 2019 paper, Co-Evolutionary Compression for unpaired image Translation, which proposes a co-evolutionary approach for reducing memory usage and FLOPs of generators on image-to-image transfer task simultaneously while maintains their performances.

Performance on cityscapes compared with conventional pruning method:

A Pytorch implementation for our NeurIPS 2020 paper, SCOP: Scientific Control for Reliable Neural Network Pruning, which proposes a reliable neural network pruning algorithm by setting up a scientific control.

Comparison of the pruned networks with different methods on ImageNet.