How does the performance of NOVA's open-world recognition capability compare to existing OOD detection methods

Abstract

Abstract: Mahalanobis distance (MD) is a simple and popular post-processing method for detecting out-of-distribution (OOD) inputs in neural networks. We analyze its failure modes for near-OOD detection and propose a simple fix called relative Mahalanobis distance (RMD) which improves performance and is more robust to hyperparameter choice. On a wide selection of challenging vision, language, and biology OOD benchmarks (CIFAR-100 vs CIFAR-10, CLINC OOD intent detection, Genomics OOD), we show that RMD meaningfully improves upon MD performance (by up to 15\% AUROC on genomics OOD).

Research Question

How does the performance of NOVA's open-world recognition capability compare to existing OOD detection methods like ODIN and Mahalanobis distance when evaluated on standard benchmarks such as CIFAR-100 and ImageNet-30?

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