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Learning scale-variant and scale-invariant features for deep image classification
2017
Pattern Recognition
Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invariant CNN representations. However, scale-invariant representations are incomplete representations of
doi:10.1016/j.patcog.2016.06.005
fatcat:yqfdq5yo5bdrtpn765mhmxolfe