On characterizing the evolution of embedding space of neural networks using algebraic topology

Published: 01 Jan 2024, Last Modified: 10 Feb 2025Pattern Recognit. Lett. 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•This paper categorizes the change in topological complexity through Betti-Numbers.•Attempts to characterize the learning ability of the DNNs through topological transformation of feature space.•Attempts to use the topological complexity of feature space for the task of ranking pre-trained models.
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