Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data

TL;DR

This paper introduces a UCB-based training method with a dynamic usability metric to improve synthetic data utilization in supervised learning, significantly boosting classifier performance.

Contribution

It presents a novel UCB-based training procedure and a dynamic usability metric for synthetic data, enhancing model training and data generation processes.

Findings

Up to 10% improvement in classification accuracy.

Effective ranking of synthetic images based on usability.

Enhanced synthetic data generation with attribute-aware bandit pipeline.

Abstract

Supervised machine learning methods require large-scale training datasets to perform well in practice. Synthetic data has been showing great progress recently and has been used as a complement to real data. However, there is yet a great urge to assess the usability of synthetically generated data. To this end, we propose a novel UCB-based training procedure combined with a dynamic usability metric. Our proposed metric integrates low-level and high-level information from synthetic images and their corresponding real and synthetic datasets, surpassing existing traditional metrics. By utilizing a UCB-based dynamic approach ensures continual enhancement of model learning. Unlike other approaches, our method effectively adapts to changes in the machine learning model's state and considers the evolving utility of training samples during the training process. We show that our metric is an effective way to rank synthetic images based on their usability. Furthermore, we propose a new attribute-aware bandit pipeline for generating synthetic data by integrating a Large Language Model with Stable Diffusion. Quantitative results show that our approach can boost the performance of a wide range of supervised classifiers. Notably, we observed an improvement of up to 10% in classification accuracy compared to traditional approaches, demonstrating the effectiveness of our approach. Our source code, datasets, and additional materials are publically available at https://github.com/A-Kerim/Synthetic-Data-Usability-2024.