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CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary visual recognition system makes use of fine-tuning to transfer knowledge from ImageNet. Indoi:10.1109/cvpr.2017.323 dblp:conf/cvpr/WangRH17 fatcat:xvmwcemhjjbf7aqsyxkdem756m