This repository is the official PyTorch implementation of Adversarial Mirrored Autoencoder. Find the paper on arxiv. To appear in Uncertainty in Artificial Intelligence (UAI 2021).

python train.py --recon_loss_type='wasserstein' --ae_recon_loss_type='wasserstein' --use_penalty --spectral_norm=1 --anom_recon_lambda=5 --regularizer_lambda=1 --anom_pc=0.1 --dataset='cifar10' --expt_name='cifar_experiments' --normal_class="['ship']" --sampling

For OOD experiments run

python train.py --recon_loss_type='wasserstein' --ae_recon_loss_type='wasserstein' --use_penalty --spectral_norm=1 --anom_recon_lambda=5 --regularizer_lambda=1 --anom_pc=0.1 --dataset='cifar10' --expt_name='cifar_experiments' --ood_model --sampling 

• --dataset - Currently works with CIFAR10.
• --ood_model - Call this when you want to run OOD Anomaly Detection experiment.
• --sampling - Turn sampling on/off using this
• --atyp_selec_style - Which style sampling for anomalies. Options are 'inward|outward|sipple'
• --interpolation_in_recon - Turns on simplex interpolation in training
• --corrup - Turn this on for corruption of training data. Use it along with --anom_pc to choose the level of corruption
• --anom_pc - If used without --corrup, it results in Semi-supervised learning.
• --normal_class - To select which class is normal in in-distribution AD experiments. Multiple classes can be selected.
• --nz - Size of the latent space variable. 128 for CIFAR10 expts.

• Python3.7 and the most recent pytorch.
• wandb installed and initialized to visualize the trends

The best model is chosen based on AUC on validation data. The test AUC and other information are outputted as summary parameter on wandb.

Other option - the auc scores and reconstructions are outputted into a text file in logs folder.

We would like to thank the following public repos from which we borrowed various utilites.

• https://github.com/christiancosgrove/pytorch-spectral-normalization-gan
• https://github.com/eriklindernoren/PyTorch-GAN

This repository is released under the Apache 2.0 license as found in the LICENSE file.