Transfer-Based Semantic Anomaly Detection

This repository contains PyTorch code for two transfer-based anomaly detection (AD) models:

• AD with an inductive bias (ADIB),
• AD with residual adaptation (ADRA).

Change the configuration in util/parser.py or by launching train.py with custom commands, e.g. to train ADRA on class seven (horse) of the CIFAR-10 one-versus-rest AD benchmark:

python3 train.py --model adra --normal_class 7

ADIB uses L2SP to regularize model weights and is active by default. For experiments on the CIFAR-10 semantic AD benchmark, set --benchmark hold_one_out.

Data will initially be downloaded to data unless some path is provided via config.data_path that contains both CIFAR-10 and CIFAR-100. Both models require pretrained weights, which will download automatically provided the prerequisite gdown has been installed.

If you find that this code is useful in your research, please cite our work as:

@inproceedings{deecke21,
    author       = "Deecke, Lucas and Ruff, Lukas and Vandermeulen, Robert A. and Bilen, Hakan",
    title        = "Transfer-Based Semantic Anomaly Detection",
    booktitle    = "International Conference on Machine Learning",
    year         = "2021"
}