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This work addresses the problem of constructing an effective training set at minimal labeling cost by selecting some images to build a subset from the whole database. This problem occurs in situations that the number of categories is large or the cost of obtaining labeled images is extremely high, because the images selected by uniform sampling do not reflect the desired training distribution and...
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