  Detecting out-of-distribution (OOD) samples plays a key role in open-world
and safety-critical applications such as autonomous systems and healthcare.
Recently, self-supervised representation learning techniques (via contrastive
learning and pretext learning) have shown effective in improving OOD detection.
However, one major issue with such approaches is the choice of shifting
transformations and pretext tasks which depends on the in-domain distribution.
In this paper, we propose a simple framework that leverages a shifting
transformation learning setting for learning multiple shifted representations
of the training set for improved OOD detection. To address the problem of
selecting optimal shifting transformation and pretext tasks, we propose a
simple mechanism for automatically selecting the transformations and modulating
their effect on representation learning without requiring any OOD training
samples. In extensive experiments, we show that our simple framework
outperforms state-of-the-art OOD detection models on several image datasets. We
also characterize the criteria for a desirable OOD detector for real-world
applications and demonstrate the efficacy of our proposed technique against
state-of-the-art OOD detection techniques.
