GAN Pruning Save
Code for "Co-Evolutionary Compression for Unpaired Image Translation" (ICCV 2019), "SCOP: Scientific Control for Reliable Neural Network Pruning" (NeurIPS 2020) and “Manifold Regularized Dynamic Network Pruning” (CVPR 2021).
GAN-pruning
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
Performance on cityscapes compared with conventional pruning method:
SCOP
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.
Performance
Comparison of the pruned networks with different methods on ImageNet.
ManiDP
A Pytorch implementation for our CVPR 2021 paper, Manifold Regularized Dynamic Network Pruning, which proposes a dynamic pruning paradigm to maximally excavate network redundancy corresponding to input instances.
Performance
Comparison of the pruned networks with different methods on ImageNet.
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