  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 RODD outperforms SOTA detection
performance on an extensive suite of benchmark datasets on OOD detection tasks.
On the CIFAR-100 benchmarks, RODD achieves a 26.97 $\%$ lower false-positive
rate (FPR@95) compared to SOTA methods.
