Clothing1M

Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the generalization of models and result in memorizing samples, further learning erroneous associations of data contents to incorrect annotations.

Data and Resources

Cite this as

Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A.K. Suykens (2024). Dataset: Clothing1M. https://doi.org/10.57702/czdkdzvd

DOI retrieved: November 25, 2024

Additional Info

Field Value
Created November 25, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2003.02752
Citation
  • https://doi.org/10.48550/arXiv.2401.04993
  • https://doi.org/10.48550/arXiv.1909.03388
  • https://doi.org/10.48550/arXiv.2404.01853
  • https://doi.org/10.48550/arXiv.1808.01097
  • https://doi.org/10.48550/arXiv.2307.04312
  • https://doi.org/10.48550/arXiv.2112.01197
  • https://doi.org/10.48550/arXiv.2105.03059
  • https://doi.org/10.48550/arXiv.2106.15185
Author Yingyi Chen
More Authors
Xi Shen
Yahui Liu
Qinghua Tao
Johan A.K. Suykens
Homepage https://clothing1m.com/