This repository consists PyTorch code for deep neural networks with graph interpolating function as output activation function
External dependency: pyflann (https://github.com/primetang/pyflann)
Place the pyflann library in your current directory to replace the pyflann folder
Code for reproducing results of naturally trained ResNets on the Cifar10
Code for reproducing results of PGD adversarial training for ResNets on the Cifar10
Code for reproducing results of PGD adversarial training for Small-CNN on the MNIST
If you find this work useful and use it on you own research, please cite our paper
@incollection{NIPS2018_7355,
title = {Deep Neural Nets with Interpolating Function as Output Activation},
author = {Wang, Bao and Luo, Xiyang and Li, Zhen and Zhu, Wei and Shi, Zuoqiang and Osher, Stanley},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {743--753},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7355-deep-neural-nets-with-interpolating-function-as-output-activation.pdf}
}
And the longer version is available at
@ARTICLE{Wang:2019Interpolation,
author = {B. Wang and S. Osher},
title = "{Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning}",
journal = {arXiv e-prints},
year = "2019",
month = "Jul",
eid = {arXiv:1907.06800},
pages = {arXiv:1907.06800},
archivePrefix = {arXiv},
eprint = {},
primaryClass = {stat.ML}
}
PyTorch 0.4.1