Deep-SVDD-PyTorch:Deep SVDD异常检测方法的PyTorch实现

# PyTorch Implementation of Deep SVDD This repository provides a [PyTorch](https://pytorch.org/) implementation of the *Deep SVDD* method presented in our ICML 2018 paper ”Deep One-Class Classification”. ## Citation and Contact You find a PDF of the Deep One-Class Classification ICML 2018 paper at [http://proceedings.mlr.press/v80/ruff18a.html](http://proceedings.mlr.press/v80/ruff18a.html). If you use our work, please also cite the paper: ``` @InProceedings{pmlr-v80-ruff18a, title = {Deep One-Class Classification}, author = {Ruff, Lukas and Vandermeulen, Robert A. and G{\"o}rnitz, Nico and Deecke, Lucas and Siddiqui, Shoaib A. and Binder, Alexander and M{\"u}ller, Emmanuel and Kloft, Marius}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {4393--4402}, year = {2018}, volume = {80}, } ``` If you would like to get in touch, please contact [contact@lukasruff.com](mailto:contact@lukasruff.com). ## Abstract > > Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of > > deep learning approaches for anomaly detection. Those approaches which do exist involve networks trained to perform > > a task other than anomaly detection, namely generative models or compression, which are in turn adapted for use in > > anomaly detection; they are not trained on an anomaly detection based objective. In this paper we introduce a new > > anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based > > objective. The adaptation to the deep regime necessitates that our neural network and training procedure satisfy > > certain properties, which we demonstrate theoretically. We show the effectiveness of our method on MNIST and > > CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs. ## Installation This code is written in `Python 3.7` and requires the packages listed in `requirements.txt`. Clone the repository to your local machine and directory of choice: ``` git clone https://github.com/lukasruff/Deep-SVDD-PyTorch.git ``` To run the code, we recommend setting up a virtual environment, e.g. using `virtualenv` or `conda`: ### `virtualenv` ``` # pip install virtualenv cd <path-to-Deep-SVDD-PyTorch-directory> virtualenv myenv source myenv/bin/activate pip install -r requirements.txt ``` ### `conda` ``` cd <path-to-Deep-SVDD-PyTorch-directory> conda create --name myenv source activate myenv while read requirement; do conda install -n myenv --yes $requirement; done < requirements.txt ``` ## Running experiments We currently have implemented the MNIST ([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) and CIFAR-10 ([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)) datasets and simple LeNet-type networks. Have a look into `main.py` for all possible arguments and options. ### MNIST example ``` cd <path-to-Deep-SVDD-PyTorch-directory> # activate virtual environment source myenv/bin/activate # or 'source activate myenv' for conda # create folder for experimental output mkdir log/mnist_test # change to source directory cd src # run experiment python main.py mnist mnist_LeNet ../log/mnist_test ../data --objective one-class --lr 0.0001 --n_epochs 150 --lr_milestone 50 --batch_size 200 --weight_decay 0.5e-6 --pretrain True --ae_lr 0.0001 --ae_n_epochs 150 --ae_lr_milestone 50 --ae_batch_size 200 --ae_weight_decay 0.5e-3 --normal_class 3; ``` This example trains a One-Class Deep SVDD model where digit 3 (`--normal_class 3`) is considered to be the normal class. Autoencoder pretraining is used for parameter initialization. ### CIFAR-10 example ``` cd <path-to-Deep-SVDD-PyTorch-directory> # activate virtual environment source myenv/bin/activate # or 'source activate myenv' for conda # create folder for experimental output mkdir log/cifar10_test # change to source directory cd src # run experiment python main.py cifar10 cifar10_LeNet ../log/cifar10_test ../data --objective one-class --lr 0.0001 --n_epochs 150 --lr_milestone 50 --batch_size 200 --weight_decay 0.5e-6 --pretrain True --ae_lr 0.0001 --ae_n_epochs 350 --ae_lr_milestone 250 --ae_batch_size 200 --ae_weight_decay 0.5e-6 --normal_class 3; ``` This example trains a One-Class Deep SVDD model where cats (`--normal_class 3`) is considered to be the normal class. Autoencoder pretraining is used for parameter initialization. ## Examples ### MNIST Example of the 32 most normal (left) and 32 most anomalous (right) test set examples per class on MNIST according to Deep SVDD anomaly scores.  ### CIFAR-10 Example of the 32 most normal (left) and 32 most anomalous (right) test set examples per class on CIFAR-10 according to Deep SVDD anomaly scores.  ## License MIT