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Do Better ImageNet Models Transfer Better?
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks aredoi:10.1109/cvpr.2019.00277 dblp:conf/cvpr/KornblithSL19 fatcat:nylanmer5zgajd6ry3iyygbu5i