anas-awadalla/gram-ood-detection

Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices

Code for NeurIPS 2019 Workshop on Safety and Robustness in Decision Making paper: https://arxiv.org/abs/1912.12510.pdf

Dependencies

The code is written in Python 3 with Pytorch 1.1.

Results

(Please refer to this repository for the results of Baseline/ODIN/Mahalanobis on dataset-pairs not presented in the Mahalanobis paper)

Combining Outlier Exposure (OE) and Ours

Downloading Out-of-Distribution Datasets and Pre-trained Models

We used the out-of-distribution datasets presented in odin-pytorch

We used pre-trained neural networks open-sourced by Mahalanobis and odin-pytorch. The DenseNets trained on CIFAR-10 and CIFAR-100 are by ODIN; remaining are by Mahalanobis.

For experiments on OE-trained networks, we used the pre-trained networks open-sourced by OE

Running the setup.sh downloads the Out-of-Distribution Datasets and pre-trained models.