Multi task Transformation Learning for Robust Out of Distribution Detection
Detecting out of distribution (OOD) samples plays a key role in open world and safety critical applications such as autonomous systems and healthcare. Selfsupervised representation learning techniques (e.g. contrastive learning and pretext learning) are well suited for learning representation that can identify OOD samples. In this paper, we propose a simple framework that leverages multi task transformation learning for training effective representation for OOD detection which outperforms state of the art OOD detection performance and robustness on several image datasets. We empirically observe that the OOD performance depends on the choice of data transformations which itself depends on the in domain training set. To address this problem, we propose a simple mechanism for selecting the transformations automatically and modulate their effect on representation learning without requiring any OOD training samples. We characterize the criteria for a desirable OOD detector for real world applications and demonstrate the efficacy of our proposed technique against a diverse range of the state of the art OOD detection techniques.
ArXiv