Awesome

Likelihood-Regret

Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020.

Training

To train the VAEs, use appropriate arguments and run this command:

python train_pixel.py

Evaluation

To evaluate likelihood regret's OOD detection performance, run

python compute_LR.py

To evaluate likelihood ratio, run

python test_likelihood_ratio.py

To evaluate input complexity, run

python test_inputcomplexity.py

Above commands will save the numpy arrays containing the OOD scores for in-distribution and OOD samples in specific location, and to compute aucroc score, run

python aucroc.py

Pre-trained Models

You can download pretrained VAE models on FMNIST and CIFAR-10 here.