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Jul, 2020
对抗鲁棒性 ImageNet 模型是否具有更好的迁移能力?
Do Adversarially Robust ImageNet Models Transfer Better?
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Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry
TL;DR
本研究证明,相较于标准预训练模型,鲁棒性较强的预训练模型在转移学习中表现更佳。通过研究图像分类任务,发现鲁棒性较强的ImageNet分类器在一系列标准下游任务中得到了更高的准确性。
Abstract
transfer learning
is a widely-used paradigm in
deep learning
, where models pre-trained on standard datasets can be efficiently adapted to downstream tasks. Typically, better pre-trained models yield better transf
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