The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset . Further information on the dataset contents and conversion process can be found in the paper available at https://arxiv.org/abs/1702.05373v1.

References in zbMATH (referenced in 10 articles )

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  1. Arseev, S. P.; Mestetskiy, L. M.: Character skeleton as a pen trace model for recognition from reconstructed trace (2021)
  2. Chang, Woonyoung; Ahn, Jeongyoun; Jung, Sungkyu: Double data piling leads to perfect classification (2021)
  3. Jones, Ilenna Simone; Kording, Konrad Paul: Might a single neuron solve interesting machine learning problems through successive computations on its dendritic tree? (2021)
  4. Liang, Senwei; Khoo, Yuehaw; Yang, Haizhao: Drop-activation: implicit parameter reduction and harmonious regularization (2021)
  5. Mizutani, Tomohiko: Convex programming based spectral clustering (2021)
  6. Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr: FedML: A Research Library and Benchmark for Federated Machine Learning (2020) arXiv
  7. Erway, Jennifer B.; Griffin, Joshua; Marcia, Roummel F.; Omheni, Riadh: Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations (2020)
  8. Castro, Daniel C.; Tan, Jeremy; Kainz, Bernhard; Konukoglu, Ender; Glocker, Ben: Morpho-MNIST: quantitative assessment and diagnostics for representation learning (2019)
  9. Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios: Neural Spline Flows (2019) arXiv
  10. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)