Fashion-MNIST

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at this https URL

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References in zbMATH (referenced in 9 articles )

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  1. Cui, Zhenghang; Charoenphakdee, Nontawat; Sato, Issei; Sugiyama, Masashi: Classification from triplet comparison data (2020)
  2. Duan, Shiyu; Yu, Shujian; Chen, Yunmei; Principe, Jose C.: On kernel method-based connectionist models and supervised deep learning without backpropagation (2020)
  3. Gu, Xue; Meng, Ziyao; Liang, Yanchun; Xu, Dong; Huang, Han; Han, Xiaosong; et al.: ESAE: evolutionary strategy-based architecture evolution (2020)
  4. Kang, Dongseok; Ahn, Chang Wook: Efficient neural network space with genetic search (2020)
  5. Zhuang, Huiping; Lin, Zhiping; Toh, Kar-Ann: Training a multilayer network with low-memory kernel-and-range projection (2020)
  6. Castro, Daniel C.; Tan, Jeremy; Kainz, Bernhard; Konukoglu, Ender; Glocker, Ben: Morpho-MNIST: quantitative assessment and diagnostics for representation learning (2019)
  7. Cowen, Benjamin; Saridena, Apoorva Nandini; Choromanska, Anna: LSALSA: accelerated source separation via learned sparse coding (2019)
  8. Kovachki, Nikola B.; Stuart, Andrew M.: Ensemble Kalman inversion: a derivative-free technique for machine learning tasks (2019)
  9. Li, Qianxiao; Chen, Long; Tai, Cheng; E, Weinan: Maximum principle based algorithms for deep learning (2018)