  Current techniques for Out-of-Distribution (OoD) detection predominantly rely
on quantifying predictive uncertainty and incorporating model regularization
during the training phase, using either real or synthetic OoD samples. However,
methods that utilize real OoD samples lack exploration and are prone to overfit
the OoD samples at hand. Whereas synthetic samples are often generated based on
features extracted from training data, rendering them less effective when the
training and OoD data are highly overlapped in the feature space. In this work,
we propose a Wasserstein-score-based generative adversarial training scheme to
enhance OoD detection accuracy, which, for the first time, performs data
augmentation and exploration simultaneously under the supervision of limited
OoD samples. Specifically, the generator explores OoD spaces and generates
synthetic OoD samples using feedback from the discriminator, while the
discriminator exploits both the observed and synthesized samples for OoD
detection using a predefined Wasserstein score. We provide theoretical
guarantees that the optimal solutions of our generative scheme are
statistically achievable through adversarial training in empirical settings. We
then demonstrate that the proposed method outperforms state-of-the-art
techniques on various computer vision datasets and exhibits superior
generalizability to unseen OoD data.
