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A curated, but incomplete, list of data-centric AI resources.
A curated, but incomplete, list of data-centric AI resources. It should be noted that it is unfeasible to encompass every paper. Thus, we prefer to selectively choose papers that present a range of distinct ideas. We welcome contributions to further enrich and refine this list.
:loudspeaker: News: Please check out our open-sourced Large Time Series Model (LTSM)!
If you want to contribute to this list, please feel free to send a pull request. Also, you can contact daochen.zha@rice.edu.
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datacentriczdcwhu and add a note indicating that you want to join the Data-centric AI group)!Data-centric AI is an emerging field that focuses on engineering data to improve AI systems with enhanced data quality and quantity.
In the conventional model-centric AI lifecycle, researchers and developers primarily focus on identifying more effective models to improve AI performance while keeping the data largely unchanged. However, this model-centric paradigm overlooks the potential quality issues and undesirable flaws of data, such as missing values, incorrect labels, and anomalies. Complementing the existing efforts in model advancement, data-centric AI emphasizes the systematic engineering of data to build AI systems, shifting our focus from model to data.
It is important to note that "data-centric" differs fundamentally from "data-driven", as the latter only emphasizes the use of data to guide AI development, which typically still centers on developing models rather than engineering data.
Two motivating examples of GPT models highlight the central role of data in AI.
Another example is Segment Anything, a foundation model for computer vision. The core of training Segment Anything lies in the large amount of annotated data, containing more than 1 billion masks, which is 400 times larger than existing segmentation datasets.
Data-centric AI framework consists of three goals: training data development, inference data development, and data maintenance, where each goal is associated with several sub-goals.
Zha, Daochen, et al. "Data-centric Artificial Intelligence: A Survey." arXiv preprint arXiv:2303.10158, 2023.
@article{zha2023data-centric-survey,
title={Data-centric Artificial Intelligence: A Survey},
author={Zha, Daochen and Bhat, Zaid Pervaiz and Lai, Kwei-Herng and Yang, Fan and Jiang, Zhimeng and Zhong, Shaochen and Hu, Xia},
journal={arXiv preprint arXiv:2303.10158},
year={2023}
}
Zha, Daochen, et al. "Data-centric AI: Perspectives and Challenges." SDM, 2023.
@inproceedings{zha2023data-centric-perspectives,
title={Data-centric AI: Perspectives and Challenges},
author={Zha, Daochen and Bhat, Zaid Pervaiz and Lai, Kwei-Herng and Yang, Fan and Hu, Xia},
booktitle={SDM},
year={2023}
}
Selected from shared topics, language and repository description—not editorial ratings.
TakedoNick /
A curated knowledge graph mapping the conceptual evolution of Artificial Intelligence, designed to help learners and researchers understand not just what happened in AI, but why each breakthrough emerged.