Shifting Transformation Learning for Out-of-Distribution Detection
This addresses a key issue in open-world and safety-critical applications like autonomous systems and healthcare by improving OOD detection, though it is incremental as it builds on existing self-supervised learning techniques.
The paper tackles the problem of selecting optimal shifting transformations and pretext tasks for out-of-distribution (OOD) detection by proposing a framework that automatically learns multiple shifted representations without OOD training samples, and it outperforms state-of-the-art models on several image datasets.
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.