GenDataAgent: On-the-fly Dataset Augmentation with Synthetic Data
Zhiteng Li Lele Chen Jerone Andrews Yunhao Ba Yulun Zhang Alice Xiang
We propose a generative agent that augments training datasets with synthetic data for model fine-tuning. Unlike prior work, which uniformly samples synthetic data, our agent iteratively generates relevant samples on-the-fly, aligning with the target distribution. It prioritizes synthetic data that complements difficult training samples, focusing on those with high variance in gradient updates. Experiments across several image classification tasks demonstrate the effectiveness of our approach.
supervised learning, classification, computer vision, synthetic data, generative AI, responsible AI, fairness
GenDataAgent is an on-the-fly generative agent that augments training datasets with task-relevant synthetic data, improving model fine-tuning and classification performance