Abstract
Recent studies have addressed the concern of detecting and rejecting the out-of-distribution (OOD) samples as a major challenge in the safe deployment of deep learning (DL) models. It is desired that the DL model should only be confident about the in-distribution (ID) data which reinforces the driving principle of the OOD detection. In this paper, we propose a simple yet effective generalized OOD detection method independent of out-of-distribution datasets. Our approach relies on self-supervised feature learning of the training samples, where the embeddings lie on a compact low-dimensional space. Motivated by the recent studies that show self-supervised adversarial contrastive learning helps robustify the model, we empirically show that a pre-trained model with self-supervised contrastive learning yields a better model for uni-dimensional feature learning in the latent space. The method proposed in this work referred to as \texttt{RODD}, outperforms SOTA detection performance on an extensive suite of benchmark datasets on OOD detection tasks. On the CIFAR-100 benchmarks, \texttt{RODD} achieves a 26.97 $\%$ lower false-positive rate (FPR@95) compared to SOTA methods.
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URL
https://arxiv.org/abs/2204.02553
