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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.

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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/