Self-Supervised Learning for Generalizable Out-of-Distribution Detection

TLDR

The real‑world deployment of DNNs in safety‑critical applications such as autonomous vehicles requires robust detection and rejection of out‑of‑distribution outliers that could cause fatal errors. We propose a self‑supervised technique for generalizable OOD feature learning that rejects such samples at inference time. The method learns OOD features without prior knowledge of target OOD distributions and incurs no extra overhead compared to existing approaches. Experiments on image classification show the technique outperforms state‑of‑the‑art OOD detectors and also reduces in‑distribution classification risk by rejecting samples near the training boundary.

Abstract

The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as autonomous vehicles needs to address a variety of DNNs' vulnerabilities, one of which being detecting and rejecting out-of-distribution outliers that might result in unpredictable fatal errors. We propose a new technique relying on self-supervision for generalizable out-of-distribution (OOD) feature learning and rejecting those samples at the inference time. Our technique does not need to pre-know the distribution of targeted OOD samples and incur no extra overheads compared to other methods. We perform multiple image classification experiments and observe our technique to perform favorably against state-of-the-art OOD detection methods. Interestingly, we witness that our method also reduces in-distribution classification risk via rejecting samples near the boundaries of the training set distribution.