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Characterization of AI Model Configurations For Model Reuse

Published

Author(s)

Peter Bajcsy, Daniel Gao, Michael Paul Majurski, Thomas Cleveland, Manuel Carrasco, Michael Buschmann, Walid Keyrouz

Abstract

With the widespread creation of artificial intelligence (AI) models in biosciences, bio-medical researchers are reusing trained AI models from other applications. This work is motivated by the need to characterize trained AI models for reuse based on metrics derived from optimization curves captured during model training. Such AI model characterizations can aid future model accuracy refinement, inform users about model hyper-parameter sensitivity, and assist in model reuse according to multi-purpose objectives. The challenges lie in understanding relationships between trained AI models and optimization curves, defining and validating quantitative AI model metrics, and disseminating metrics with trained AI models. We approach these challenges by analyzing optimization curves generated for image segmentation and classification tasks to assist in a multi-objective reuse of AI models.
Proceedings Title
Proceedings of the European Conference on Computer Vision (ECCV), Bio Image Computing workshop
Conference Dates
October 24-28, 2022
Conference Location
Tel Aviv, IL
Conference Title
Bio Image Computing workshop, URL: https://www.bioimagecomputing.com/

Keywords

Deep Learning Model Reuse, Deep Learning for Images, Optimization curves from AI Model Training

Citation

Bajcsy, P. , Gao, D. , Majurski, M. , Cleveland, T. , Carrasco, M. , Buschmann, M. and Keyrouz, W. (2022), Characterization of AI Model Configurations For Model Reuse, Proceedings of the European Conference on Computer Vision (ECCV), Bio Image Computing workshop , Tel Aviv, IL, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934241 (Accessed April 18, 2024)
Created October 24, 2022, Updated January 6, 2023