Code for NeurIPS paper: "HRN: A Holistic Approach to One Class Learning"
Some important packages' versions are as follow:
scikit-learn == 0.21.3
torch == 1.2.0
You can run our code on MNIST directly by this instruction "python3 main.py". Meaning of the arguments:
--max_epochs: the number of epochs of training
--batch_size: the size of the batches
--lr: the learning rate of the adam optimizer
--n_cpu: the number of cpu threads to use during batch generation
--img_size: the lenth of input image vectors (eg. mnist is 28*28=784)
--num_classes: the number of classes of the dataset
--gpu: choose whether to use gpu
--dataset: choose dataset for experiments
Please cite our paper the code helps you, thanks very much.
@article{hu2020hrn,
title={HRN: A Holistic Approach to One Class Learning},
author={Hu, Wenpeng and Wang, Mengyu and Qin, Qi and Ma, Jinwen and Liu, Bing},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}