NICO: A Dataset Towards Non-I.I.D. Image Classification
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The I.I.D. hypothesis between training data and testing data is the basis of a large number of image classification methods. Such a property can hardly be guaranteed in practical cases where the Non-IIDness is common, leading to instable performances of these models. In literature, however, the Non-I.I.D. image classification problem is largely understudied. A key reason is the lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I.D. image dataset called NICO, which makes use of contexts to create Non-IIDness consciously. Extended experimental results and anslyses demonstrate that the NICO dataset can well support the training of a ConvNet model from scratch, and NICO can support various Non-I.I.D. situations with sufficient flexibility compared to other datasets.
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