Characterization of AI Model Configurations For Model Reuse

Anonymous

Keywords: Optimization and learning methods, Efficient training and inference methods, Medical, biological, and cell microscopy

TL;DR: Reuse of AI models based on characteristics derived from optimization curves

Abstract:

With the widespread creation of artificial intelligence (AI) models in biosciences, researchers are reusing AI models trained for specific tasks.
This work is motivated by the need to characterize AI models for reuse and dissemination based on metrics derived from optimization curves captured during model training.
Such AI model characterization 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 AI model characteristics 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 with respect to AI model characteristics reused for many purposes.