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Fashion-MNIST

Deep learning models often raise privacy concerns as they leak information about their training data. This enables an adversary to determine whether a data point was in a model’s training set by conducting a membership inference attack (MIA).

Data and Resources

Cite this as

Han Xiao, Kashif Rasul, Roland Vollgraf (2024). Dataset: Fashion-MNIST. https://doi.org/10.57702/09tnix2m

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.2002.04829
Citation
  • https://doi.org/10.48550/arXiv.2404.10296
  • https://doi.org/10.1016/j.ins.2024.120500
  • https://doi.org/10.48550/arXiv.2105.06246
  • https://doi.org/10.48550/arXiv.2206.12342
  • https://doi.org/10.48550/arXiv.2310.03398
  • https://doi.org/10.48550/arXiv.2308.16665
  • https://doi.org/10.48550/arXiv.1805.07674
  • https://doi.org/10.48550/arXiv.1909.03388
  • https://doi.org/10.48550/arXiv.2310.03833
  • https://doi.org/10.48550/arXiv.1912.03049
  • https://doi.org/10.48550/arXiv.2008.09845
  • https://doi.org/10.48550/arXiv.1908.03015
  • https://doi.org/10.48550/arXiv.2004.03749
  • https://doi.org/10.48550/arXiv.2403.03149
  • https://doi.org/10.48550/arXiv.1805.05269
  • https://doi.org/10.48550/arXiv.1804.06537
  • https://doi.org/10.48550/arXiv.1904.10596
  • https://doi.org/10.48550/arXiv.2308.00307
  • https://doi.org/10.5121/csit.2021.110708
  • https://doi.org/10.48550/arXiv.2006.12456
  • https://doi.org/10.48550/arXiv.1811.08484
  • https://doi.org/10.48550/arXiv.2106.10704
  • https://doi.org/10.48550/arXiv.2305.08753
  • https://doi.org/10.48550/arXiv.2201.08022
  • https://doi.org/10.48550/arXiv.2402.16294
  • https://doi.org/10.48550/arXiv.2103.08074
  • https://doi.org/10.48550/arXiv.2309.01532
  • https://doi.org/10.48550/arXiv.2305.17644
  • https://doi.org/10.48550/arXiv.1710.07138
  • https://doi.org/10.48550/arXiv.1907.09750
  • https://doi.org/10.48550/arXiv.2103.10252
  • https://doi.org/10.1109/VIS54172.2023.00058
  • https://doi.org/10.48550/arXiv.1903.09171
  • https://doi.org/10.1587/transinf.2022EDL8098
  • https://doi.org/10.48550/arXiv.1912.01530
  • https://doi.org/10.48550/arXiv.2006.09654
  • https://doi.org/10.48550/arXiv.2009.07360
  • https://doi.org/10.48550/arXiv.1908.01052
  • https://doi.org/10.48550/arXiv.1906.11632
  • https://doi.org/10.48550/arXiv.2011.05578
  • https://doi.org/10.48550/arXiv.2207.05902
  • https://doi.org/10.48550/arXiv.2302.01500
  • https://doi.org/10.1109/ICMLA.2019.00125
  • https://doi.org/10.48550/arXiv.2006.05336
  • https://doi.org/10.48550/arXiv.2306.05256
  • https://doi.org/10.48550/arXiv.1902.04697
  • https://doi.org/10.48550/arXiv.2012.07399
  • https://doi.org/10.48550/arXiv.2204.08453
  • https://doi.org/10.48550/arXiv.2309.06652
  • https://doi.org/10.48550/arXiv.1905.09523
  • https://doi.org/10.1109/IJCNN52387.2021.9533862
  • https://doi.org/10.48550/arXiv.2112.06832
  • https://doi.org/10.48550/arXiv.2004.02581
  • https://doi.org/10.48550/arXiv.2301.06957
  • https://doi.org/10.48550/arXiv.2111.15099
  • https://doi.org/10.48550/arXiv.1806.03796
  • https://doi.org/10.48550/arXiv.1910.02760
  • https://doi.org/10.48550/arXiv.2207.09531
  • https://doi.org/10.48550/arXiv.1902.08297
  • https://doi.org/10.48550/arXiv.2205.15523
  • https://doi.org/10.1016/j.array.2022.100182
  • https://doi.org/10.1109/TII.2022.3170348
  • https://doi.org/10.48550/arXiv.2006.04535
  • https://doi.org/10.1109/LSP.2019.2915661
  • https://doi.org/10.48550/arXiv.2205.02887
  • https://doi.org/10.48550/arXiv.2107.12547
  • https://doi.org/10.48550/arXiv.2102.10749
  • https://doi.org/10.48550/arXiv.2306.05497
Author Han Xiao
More Authors
Kashif Rasul
Roland Vollgraf
Homepage https://fashion-mnist.s3.eu-west-2.amazonaws.com/