Out-of-distribution Detection with Boundary Aware Learning

There is an increasing need to determine whether inputs are out-of-distribution (OOD) for safely deploying machine learning models in the open world scenario. Typical neural classifiers are based on the closed world assumption, where the training data and the test data are drawn i.i.d. from the same distribution, and as a result, give over-confident predictions even faced with OOD inputs. For tackling this problem, previous studies either use real outliers for training or generate synthetic OOD data under strong assumptions, which are either costly or intractable to generalize. In this paper, we propose boundary aware learning (BAL), a novel framework that can learn the distribution of OOD features adaptively. The key idea of BAL is to generate OOD features from trivial to hard progressively with a generator, meanwhile, a discriminator is trained for distinguishing these synthetic OOD features and in-distribution (ID) features. Benefiting from the adversarial training scheme, the discriminator can well separate ID and OOD features, allowing more robust OOD detection. The proposed BAL achieves state-of-the-art performance on classification benchmarks, reducing up to 13.9% FPR95 compared with previous methods.