pseudo-Bayesian Neural Networks for detecting Out of Distribution Inputs

Conventional Bayesian Neural Networks (BNNs) are known to be capable of providing multiple outputs for a single input, the variations in which can be utilised to detect Out of Distribution (OOD) inputs. BNNs are difficult to train due to their sensitivity towards the choice of priors. To alleviate this issue, we propose pseudo-BNNs where instead of learning distributions over weights, we use point estimates and perturb weights at the time of inference. We modify the cost function of conventional BNNs and use it to learn parameters for the purpose of injecting right amount of random perturbations to each of the weights of a neural network with point estimate. In order to effectively segregate OOD inputs from In Distribution (ID) inputs using multiple outputs, we further propose two measures, derived from the index of dispersion and entropy of probability distributions, and combine them with the proposed pseudo-BNNs. Overall, this combination results in a principled technique to detect OOD samples at the time of inference. We evaluate our technique on a wide variety of neural network architectures and image classification datasets. We observe that our method achieves state of the art results and beats the related previous work on various metrics such as FPR at 95% TPR, AUROC, AUPR and Detection Error by just using 2 to 5 samples of weights per input.