Out-of-distribution Detection In Real-world Environments
Abstract
Modern deep neural networks have achieved remarkable success in known contexts for which they are trained. Yet, they often strugglewith unknown situations in complex open-world environments---e.g., samples from unknown classes that the network has not been exposed to during training, but can be blindly classified as a known class with high confidence in testing. To unlock the full potential of machine intelligence in open-world environments, the project will contribute new algorithmic techniques for building safe and resilient learning methods for real-world environments. New out-of-distribution (OOD) detection algorithms will be developed for imperfect data regimes, such as insufficient or imbalanced training data. Additionally, this project will develop strategies for applying algorithmic advances to Naval applications. It will demonstrate the utility of new techniques on operationally relevant data, as well as document specific use cases for existing programs. These use cases willalso identify gaps that will inform subsequent algorithmdevelopment.#Approved for Public Release.#
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 24, 2023
- Source ID
- N000142312643
Entities
People
- Sharon Li
Organizations
- Office of Naval Research
- United States Navy
- University of Wisconsin System