  In this paper, we present a novel approach that combines deep metric learning
and synthetic data generation using diffusion models for out-of-distribution
(OOD) detection. One popular approach for OOD detection is outlier exposure,
where models are trained using a mixture of in-distribution (ID) samples and
``seen" OOD samples. For the OOD samples, the model is trained to minimize the
KL divergence between the output probability and the uniform distribution while
correctly classifying the in-distribution (ID) data. In this paper, we propose
a label-mixup approach to generate synthetic OOD data using Denoising Diffusion
Probabilistic Models (DDPMs). Additionally, we explore recent advancements in
metric learning to train our models.
  In the experiments, we found that metric learning-based loss functions
perform better than the softmax. Furthermore, the baseline models (including
softmax, and metric learning) show a significant improvement when trained with
the generated OOD data. Our approach outperforms strong baselines in
conventional OOD detection metrics.
