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.
