  Identifying Out-of-distribution (OOD) data is becoming increasingly critical
as the real-world applications of deep learning methods expand. Post-hoc
methods modify softmax scores fine-tuned on outlier data or leverage
intermediate feature layers to identify distinctive patterns between
In-Distribution (ID) and OOD samples. Other methods focus on employing diverse
OOD samples to learn discrepancies between ID and OOD. These techniques,
however, are typically dependent on the quality of the outlier samples assumed.
Density-based methods explicitly model class-conditioned distributions but this
requires long training time or retraining the classifier. To tackle these
issues, we introduce \textit{FlowCon}, a new density-based OOD detection
technique. Our main innovation lies in efficiently combining the properties of
normalizing flow with supervised contrastive learning, ensuring robust
representation learning with tractable density estimation. Empirical evaluation
shows the enhanced performance of our method across common vision datasets such
as CIFAR-10 and CIFAR-100 pretrained on ResNet18 and WideResNet classifiers. We
also perform quantitative analysis using likelihood plots and qualitative
visualization using UMAP embeddings and demonstrate the robustness of the
proposed method under various OOD contexts. Code will be open-sourced post
decision.
