mlsanityData to 14 Oct 2026

Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data

arXiv 2412.05466 ↗First posted 6 Dec 2024cs.LG cs.CVcited by 0

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 7 of its 71 references that are in the map. First posted on arXiv 6 Dec 2024.

Builds on

7 of 71 references.

References · 7

  1. 2018A Note on the Inception Scorecited by 226
  2. 2022Image Data Augmentation for Deep Learning: A Surveycited by 96
  3. 2019Improving Adversarial Robustness of Ensembles with Diversity Trainingcited by 69
  4. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Imagescited by 45
  5. 2024Synthetic Data in AI: Challenges, Applications, and Ethical Implicationscited by 21
  6. 2017On Convergence and Stability of GANscited by 18
  7. 2023Metrics to Quantify Global Consistency in Synthetic Medical Imagescited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Real-Fake: Effective Training Data Synthesis Through Distribution Matching
  2. 2026Let Training Guide Selection: Online Synthetic Data Filtering via Real-Anchored Utility
  3. 2026Synthetic Data Characterization via Training Dynamics
  4. 2024Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images
  5. 2026A Real-Calibrated Synthetic-First Data Engine
  6. 2024Comprehensive Exploration of Synthetic Data Generation: A Survey
  7. 2025Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data
  8. 2024Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory