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

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Deep Learning ood vaes
Overview

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.

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Comments
  • Not Implementing IWAE?

    Not Implementing IWAE?

    Hi,

    Thanks for your good work and repo. May I ask did you implement the exact IWAE as a tighter lower bound, in case I missed it?

    See the reference: https://github.com/XavierXiao/Likelihood-Regret/blob/5517c9bac5992b116e55bb61cad8171e0d585063/train_pixel.py#L130

    opened by GloryyrolG 2
Owner
Xavier
Xavier
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