SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank

Understand

Deep neural classifiers trained with cross-entropy loss (CE loss) often suffer from poor calibration, necessitating the task of out-of-distribution (OOD) detection.

• Traditional supervised OOD detection methods require expensive manual annotation of in-distribution and OOD samples.
• To address the annotation bottleneck, we introduce SELFOOD, a self-supervised OOD detection method that requires only in-distribution samples as supervision.
• We cast OOD detection as an inter-document intra-label (IDIL) ranking problem and train the classifier with our pairwise ranking loss, referred to as IDIL loss.