System and method for cross domain generalization for industrial artificial intelligence applications class

• Main Authors: Prajapat, Rahul, Barde, Nihal Rajan, Mondal, Arnab Kumar, Modukuru, Naga Sai Pranay, Agrahari, Rishabh, Srivastava, Aditya, Kumar, Sachin, A.P, Prathosh, Galrani, Kamal, Gupta, Rushil, Shekhawat, Sanjay
• Format: Patent
• Language: English
• Published: 09.07.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

A cross domain generalization system for industrial artificial intelligence (AI) applications is disclosed. A target encoder subsystem obtains target data from a target machine product and generates lower dimensional data for obtained target data using a target artificial intelligence (AI) model. The generated lower dimensional data are corresponding to a plurality of target embeddings data. The target encoder subsystem further applies the plurality of target embeddings data into a source classifier AI model. A source classifier subsystem predicts a quality of the target machine product by generating class labels for each of the plurality of target embeddings data based on a result of the classifier AI model. The goal of the present invention is to learn features or representations such that the correlation with a label space is similar both in source and target domains while being invariant of data distributions.