Overview

The ICCV 2025 Workshop on Curated Data for Efficient Learning (CDEL) seeks to advance the understanding and development of data-centric techniques that improve the efficiency of training large-scale machine learning models. As model sizes continue to grow and data requirements scale accordingly, this workshop brings attention to the increasingly critical role of data quality, selection, and synthesis in achieving high model performance with reduced computational cost. Rather than focusing on ever-larger datasets and models, CDEL emphasizes the curation and distillation of high-value data—leveraging techniques such as dataset distillation, data pruning, synthetic data generation, and sampling optimization. These approaches aim to reduce redundancy, improve generalization, and enable learning in data-scarce regimes. The workshop will bring together researchers and practitioners from vision, language, and multimodal learning to share insights and foster collaborations around efficient, scalable, and sustainable data-driven machine learning.

Invited Speakers

Zhuang Liu
Zhuang Liu
Princeton University
8:30 – 9:15 AM
Sara Beery
Sara Beery
Massachusetts Institute of Technology
10:00 – 10:45 AM
Andrew Owens
Andrew Owens
Cornell Tech
2:15 – 3:00 PM
Phillip Isola
Phillip Isola
Massachusetts Institute of Technology
3:45 – 4:30 PM
Wei-Chiu Ma
Wei-Chiu Ma
Cornell University
4:30 – 5:15 PM

Schedule

  • Date: October 20, 2025
  • Time: 8:30 AM – 5:30 PM
  • Location: Room 304-A

Click here for full schedule!

Call for Papers

Archival Submission Portal: OpenReview

Non-Archival Submission Portal: OpenReview

We welcome submissions on all topics related to the curation of training data.
Some potential topics include:

  • Data Pruning: How can we eliminate redundant or low-quality samples from large datasets?
  • Synthetic Data: How can we use generative models to create or augment datasets?
  • Dataset Distillation: How can we learn tiny datasets of highly-efficient synthetic samples?
  • Obscure Domains: How can we train models in areas where existing data is extremely scarce?
  • Future Directions: What problems in data-centric AI can we expect in the near future?

Submission Details:
We accept submissions of both long conference-style papers (8 pages) and short extended abstracts (4 pages). Authors of accepted long papers have the option of having their work published in the ICCV workshop proceedings if they do not violate dual-submission guidelines.

We also welcome submissions of work currently in submission or recently accepted to other venues, but these will not be published in the workshop proceedings (but may still be presented at our workshop).

Please sign up here if you’d like to volunteer as a reviewer.

Deadlines

Archival:

  • Submission deadline: July 7, 2025
  • Notification: July 11, 2025
  • Camera-ready: August 18, 2025

Non-Archival:

  • Submission deadline: August 29, 2025
  • Notification: September 12, 2025
  • Camera-ready: September 19, 2025

Please contact George (gcaz@mit.edu) with any questions.

Related Workshops

Organizers

George Cazenavette
George Cazenavette
Massachusetts Institute of Technology
Kai Wang
Kai Wang
National University of Singapore
Zekai Li
Zekai Li
National University of Singapore
Xindi Wu
Xindi Wu
Princeton University
Peihao Wang
Peihao Wang
University of Texas at Austin
Ruihan Gao
Ruihan Gao
Carnegie Mellon University
Bo Zhao
Bo Zhao
Shanghai Jiao Tong University
Zhangyang Wang
Zhangyang Wang
University of Texas at Austin
Jun-Yan Zhu
Jun-Yan Zhu
Carnegie Mellon University