Reviews: Likelihood Ratios for Out-of-Distribution Detection

The authors were motivated to solve the problem of bacterial identification in the presence of out-of-distribution (OOD) examples: when a classifer is trained on known bacterial classes and deployed in the real world, it may erroneously classify yet unknown bacterial strains by assigning them to one of the exisiting classes with high confidence. Methods for OOD detection try to address this problem. The authors propose a novel statistic to identify OOD examples: Their method is based on taking the log-likelihood ratio (LLR) between a model trained on in-distribution data and a background model. For both models, autoregressive models are used -- the background model is trained on perturbed in-distribution data (where the amount of perturbation is a hyper-parameter that needs to be tuned). Combined with the assumption that the likelihood factorises into semantic and background contributions, the statistic can be approximated as the difference in log-likelihoods under both models, effectively focusing on the semantic components only.