Code of FOCA: Feature-extractor Optimization through Classifier Anonymization [1]. This source code reproduces the results (to be appeared [2]) using Wide ResNet [3] and PyramidNet [4] trained on CIFAR-10 and CIFAR-100 [5].
- computer running Linux
- NVIDIA GPU and NCCL
- Python version 3.6
- PyTorch
Use python train.py to train a non-end-to-end model by FOCA. Here are some examples setting:
train cifar10 with Wide-ResNet and single gpu. (default setting: --dataset='cifar10' --network='wide-resnet' --idGPU=[0])
$python3 train.py
Guoqing Liu and Ikuro Sato, Denso IT Laboratory, Inc.
[1] Ikuro Sato, Kohta Ishikawa, Guoqing Liu, and Masayuki Tanaka, "Breaking Inter-Layer Co-Adaptation by Classifier Anonymization", Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.
[2] To be appeared.
[3] Sergey Zagoruyko and Nikos Komodakis, "Wide Residual Networks", Proceedings of the British Machine Vision Conference (BMVC), 2016.
[4] Dongyoon Han, Jiwhan Kim, and Junmo Kim, "Deep Pyramidal Residual Networks", Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[5] Alex Krizhevskyf and Geoffrey Hinton, "Learning multiple layers of features from tiny images", Technical Report, University of Toronto, 2009.
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