Boundary Aware Learning for Out-of-distribution Detection

This paper focuses on the problem of detecting out-of-distribution (ood) samples with neural nets. In image recognition tasks, the trained classifier often gives high confidence score for input images which are remote from the in-distribution (id) data, and this has greatly limited its application in real world. For alleviating this problem, we propose a GAN based boundary aware classifier (GBAC) for generating a closed hyperspace which only contains most id data. Our method is based on the fact that the traditional neural net seperates the feature space as several unclosed regions which are not suitable for ood detection. With GBAC as an auxiliary module, the ood data distributed outside the closed hyperspace will be assigned with much lower score, allowing more effective ood detection while maintaining the classification performance. Moreover, we present a fast sampling method for generating hard ood representations which lie on the boundary of pre-mentioned closed hyperspace. Experiments taken on several datasets and neural net architectures promise the effectiveness of GBAC.