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[2R4-OS-12-03] A Comparative Visualization Method for Tuning Process of Machine Learning Model
Keywords:Visualization, Worker
Recently, many visualization methods have been presented that target information on the model itself. On the other hand, there are few methods to visualize information about the workers (annotators, model designers, and end-users) of the model. The active intervention of workers in the modeling process is effective in improving the accuracy of models, and visualization of workers is considered useful for understanding the properties of models in detail, evaluating adjustment work, and suggesting effective improvement measures. Therefore, we propose a visualization tool that focuses on the model modification history and the objectives of individual tuning operations as information about the operators. Our tool calculates the differences in data, model structure, and optimization algorithms during model tuning, and visualizes them together with the intent of the change. We present the visualization results of the history of training and testing a model for image classification.
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