Advancing Zero-Shot Learning With Fully Connected Weighted Bipartite Graphs in Machine Learning

Abstract

This chapter presents a novel method for improving zero-shot learning in ML by using fully connected weighted bipartite graphs. Problems with generalizability and adaptability plague zero-shot learning, a method that lets models identify and categorize things or ideas without any explicit training. To overcome these obstacles and greatly enhance machine learning models' ability to absorb and comprehend unknown input, this chapter investigates how fully linked weighted bipartite graphs may be integrated. A thorough introduction to zero-shot learning is provided at the outset of the investigation. It describes the method's value in the machine learning field while drawing attention to the problems with and restrictions on current approaches. Anyone involved with machine learning, whether as a researcher, practitioner, or hobbyist, will find this chapter to be an invaluable resource. It lays out the theory and some practical considerations for improving zero-shot learning with fully connected weighted bipartite graphs.