MNIST Multiview Datasets MNIST is a publicly available dataset consisting of 70, 000 images of handwritten digits distributed over ten classes. We generated 2 four-view datasets where each view is a vector of R<sup>14 x 14</sup>: MNIST<sub>1</sub>: It is generated by considering 4 quarters of image as 4 views. MNIST<sub>2</sub>: It is generated by considering 4 overlapping views around the centre of images: this dataset brings redundancy between the views.
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We introduce the Oracle-MNIST dataset, comprising of 2828 grayscale images of 30,222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image Oracle-MNIST shares the same data format with the original MNIST dataset, allowing for direct compatibility with all existing classifiers and systems, but it constitutes a more challenging classification task than MNIST. The dataset is freely available at https://github.com/wm-bupt/oracle-mnist.
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The MNIST Large Scale dataset is based on the classic MNIST dataset, but contains large scale variations up to a factor of 16. The dataset contains training data for each one of the relative size factors 1, 2 and 4 relative to the original MNIST dataset and testing data for relative scaling factors between 1/2 and 8, with a ratio
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CI-MNIST (Correlated and Imbalanced MNIST) is a variant of MNIST dataset with introduced different types of correlations between attributes, dataset features, and an artificial eligibility criterion.
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We provide multiple human annotations for each test image in Fashion-MNIST. This can be used as soft labels or probabilistic labels instead of the usual hard (single) labels.
EMNIST (extended MNIST) has 4 times more data than MNIST. It is a set of handwritten digits with a 28 x 28 format.
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ASCAD (ANSSI SCA Database) is a set of databases that aims at providing a benchmarking reference for the SCA community: the purpose is to have something similar to the MNIST database that the Machine Learning