Multivariate Triangular Quantiles for Novelty Detection
Pytorch implementation to replicate the experiments in our NIPS2019 paper "Multivariate Triangular Quantiles for Novelty Detection" (published soon).
Datasets
- Thyroid: http://odds.cs.stonybrook.edu/thyroid-disease-dataset/
- KDDCUP: http://kdd.ics.uci.edu/databases/kddcup99/kddcup.testdata.unlabeled_10_percent.gz
- MNIST/Fashion MNIST: automatically download by torchvision
Models
This implementation includes the following models for novelty detection.
- LSA: Autoencoder part in [2]
- LSA_EN[2]: Autoencoder + Density Estimator in [2]
- LSA_SOS: Autoencoder + TQM based on SOS-flow
- LSA_MAF: Autoencoder + TQM based on MAF-flow
- EN: density estimator in [2]
- SOS: TQM based on SOS-Flow [3]
- MAF: TQM based on MAF-Flow [1]
The LSA_SOS and LSA_MAF are two instances for our TQM. In our paper, we mainly apply LSA_SOS as the novelty detection model.
Environments
This code runs on Python >=3.6. Set up environment by:
pip install -r requirements.txt
Demo
In demo.sh, we give some examples to show the how to train/test a model on one dataset
Reference
Our implementation based on the following reference/code:
-
Autoencoder(https://github.com/aimagelab/novelty-detection): Pytorch implementation to replicate experiments in the CVPR19 paper "Latent Space Autoregression for Novelty Detection"
-
MAF-flow(https://github.com/ikostrikov/pytorch-flows): PyTorch implementations of Masked Autoregressive Flow
-
SOS-flow: PyTorch implementations of Sum-of-Square Polynomial Flow
[1] Papamakarios G, Pavlakou T, Murray I. Masked autoregressive flow for density estimation[C]//Advances in Neural Information Processing Systems. 2017: 2338-2347.
[2] Abati D, Porrello A, Calderara S, et al. Latent Space Autoregression for Novelty Detection[C]//International Conference on Computer Vision and Pattern Recognition. 2019.
[3] Jaini, P., Selby, K. A., & Yu, Y. (2019). Sum-of-Squares Polynomial Flow. arXiv preprint arXiv:1905.02325.