  Detecting Out-of-Distribution (OOD) samples in real world visual applications
like classification or object detection has become a necessary precondition in
today's deployment of Deep Learning systems. Many techniques have been
proposed, of which Energy based OOD methods have proved to be promising and
achieved impressive performance. We propose semantic driven energy based
method, which is an end-to-end trainable system and easy to optimize. We
distinguish in-distribution samples from out-distribution samples with an
energy score coupled with a representation score. We achieve it by minimizing
the energy for in-distribution samples and simultaneously learn respective
class representations that are closer and maximizing energy for
out-distribution samples and pushing their representation further out from
known class representation. Moreover, we propose a novel loss function which we
call Cluster Focal Loss(CFL) that proved to be simple yet very effective in
learning better class wise cluster center representations. We find that, our
novel approach enhances outlier detection and achieve state-of-the-art as an
energy-based model on common benchmarks. On CIFAR-10 and CIFAR-100 trained
WideResNet, our model significantly reduces the relative average False Positive
Rate(at True Positive Rate of 95%) by 67.2% and 57.4% respectively, compared to
the existing energy based approaches. Further, we extend our framework for
object detection and achieve improved performance.
