Out-of-Distribution (ODD) Data Detection in Machine Learning

Abstract

The University of Central Florida invention is a highly effective generalized out-of-distribution (OOD) detection method. Highly scalable, the technology is independent of OOD datasets. Once machine learning models are in real-world applications, they tend to encounter unknown (OOD) data during inference. Detecting OOD is a crucial task in safety-critical applications to ensure the safe deployment of deep learning models. The machine learning model should only be confident about the data type that has already been seen (in-distribution or ID), reinforcing the driving principle of OOD detection.

Current technologies are not generalizable since they need a set of OOD data during training to regularize the model. In contrast, the UCF framework is independent of such regularization and is not based on any particular OOD dataset. Instead, the UCF invention uses an approach that relies on self-supervised feature learning of training samples, where the embeddings lie on a compact low-dimensional space. A fully supervised fine-tuning is then done by mapping the ID class features into 1-dimensional subspace.

Partnering Opportunity: The research team is seeking partners for licensing, research collaboration, or both.

Stage of Development: Prototype available.

Benefit

Market Application

Publications

RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection, IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR'22), June 19, 20, 2022.