Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond

• URL: http://arxiv.org/abs/2408.11338v1
• Date: Wed, 21 Aug 2024 04:45:12 GMT
• Title: Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond
• Authors: Minghao Liu, Zonglin Di, Jiaheng Wei, Zhongruo Wang, Hengxiang Zhang, Ruixuan Xiao, Haoyu Wang, Jinlong Pang, Hao Chen, Ankit Shah, Hongxin Wei, Xinlei He, Zhaowei Zhao, Haobo Wang, Lei Feng, Jindong Wang, James Davis, Yang Liu,
• Abstract summary: We propose an innovative methodology that automates dataset creation with negligible cost and high efficiency. We provide open-source software that incorporates existing methods for label error detection, robust learning under noisy and biased data. We design three benchmark datasets focused on label noise detection, label noise learning, and class-imbalanced learning.
• Score: 38.89457061559469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating high-quality datasets quickly and accurately remains a challenge due to annotation errors, the substantial time and costs associated with human labor. To address these issues, we propose Automatic Dataset Construction (ADC), an innovative methodology that automates dataset creation with negligible cost and high efficiency. Taking the image classification task as a starting point, ADC leverages LLMs for the detailed class design and code generation to collect relevant samples via search engines, significantly reducing the need for manual annotation and speeding up the data generation process. Despite these advantages, ADC also encounters real-world challenges such as label errors (label noise) and imbalanced data distributions (label bias). We provide open-source software that incorporates existing methods for label error detection, robust learning under noisy and biased data, ensuring a higher-quality training data and more robust model training procedure. Furthermore, we design three benchmark datasets focused on label noise detection, label noise learning, and class-imbalanced learning. These datasets are vital because there are few existing datasets specifically for label noise detection, despite its importance. Finally, we evaluate the performance of existing popular methods on these datasets, thereby facilitating further research in the field.

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