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Scale Generalisation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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Scale generalisation implies that learning is performed at some scale(s) and testing at other scales. The scale varies in context to the scale of the generalisation.

데이터셋

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MNIST Large Scale dataset

The MNIST Large Scale dataset is based on the classic MNIST dataset, but contains large scale variations up to a factor of 16. The motivation behind creating this dataset was to enable testing the ability of different algorithms to learn in the presence of large scale variability and specifically the ability to generalise to new scales not present in the training set over wide scale ranges. 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 of $\sqrt[4]{2}$ between adjacent scales.

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.