UNKNOWN-CLASS (OUT-OF-DISTRIBUTION) DATA DETECTION IN MACHINE LEARNING MODELS

• Main Authors: Zaeemzadeh, Alireza, Rahnavard, Nazanin, Khalid, Umar
• Format: Patent
• Language: English
• Published: 21.03.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

Described, herein, relates to a system of and method for digitally monitoring a large-scale dataset on a computing device and automatically detecting, in real-time, unknown class data in order to aid a machine learning model. Once machine learning models are deployed in the real-world applications, the models tend to encounter unknown-class (i.e., out-of-distribution) (hereinafter "OOD") data during inference. Detecting out-of-distribution data is a crucial task in safety-critical applications to ensure safe deployment of deep learning models. It is desired that the machine learning model should only be confident about the type of data that has already seen in-distribution (hereinafter "ID") class data which reinforces the driving principle of the OOD detection. The system and method may rely on contrastive feature learning of the largescale datasets, where the embeddings lie on a compact low-dimensional space. Additionally self-supervised fine-tuning may then be performed by mapping an ID class feature into uni-dimensional sub-space.