Abstract:Data augmentation (DA) has been extensively studied to facilitate model optimization in many tasks. Prior DA works focus on designing augmentation operations themselves, while leaving selecting suitable samples for augmentation out of consideration. This might incur visual ambiguities and further induce training biases. In this paper, we propose an effective approach, dubbed SelectAugment, to select samples for augmentation in a deterministic and online manner based on the sample contents and the network train… Show more
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