Trustworthy DD · Optimization and training recipes
RDC
Robust Dataset Condensation using Supervised Contrastive Learning
Nicole Hee-Yeon Kim
ICCV 2025 · first public 2025-01-01
In one paragraph
RDC is an end-to-end dataset-condensation method that mitigates label noise without a separate noise-reduction preprocessing step, using a "golden MixUp contrast" that applies contrastive learning tailored for robust condensation: synthetic samples sharpen class boundaries and reduce noisy representations, while an augmentation strategy pulls clean samples out of the noisy training data to enrich the synthetic images with real-data diversity. Reports outperforming other condensation approaches on CIFAR-10/100 across symmetric, asymmetric and real-world noise.
Where it sits
- Optimization and training recipes (Orthogonal design choices)
- Setting: Image classification
BibTeX (generated; prefer the venue's official entry)
@article{kim2025robust,
title = {Robust Dataset Condensation using Supervised Contrastive Learning},
author = {Nicole Hee-Yeon Kim},
journal = {ICCV 2025},
year = {2025}
}Nearby in Optimization and training recipes
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