Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy

1 code implementation • 22 Jan 2024 • Will LeVine, Benjamin Pikus, Jacob Phillips, Berk Norman, Fernando Amat Gil, Sean Hendryx

As deep neural networks become adopted in high-stakes domains, it is crucial to be able to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely drops in performance and calibration despite high confidence.

object-detection Object Detection +2