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.
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.
Comparison of the pruned networks with different methods on ImageNet.
