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arXiv · 2007.06965

Automated Synthetic-to-Real Generalization

Wuyang Chen·Zhiding Yu·Zhangyang Wang·Anima Anandkumar
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Abstract

Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation. Yet the role of ImageNet knowledge is seldom discussed despite common practices that leverage this knowledge to maintain the generalization ability. An example is the careful hand-tuning of early stopping and layer-wise learning rates, which is shown to improve synthetic-to-real generalization but is also laborious and heuristic. In this work, we explicitly encourage the synthetically trained model to maintain similar representations with the ImageNet pre-trained model, and propose a \textit{learning-to-optimize (L2O)} strategy to automate the selection of layer-wise learning rates. We demonstrate that the proposed framework can significantly improve the synthetic-to-real generalization performance without seeing and training on real data, while also benefiting downstream tasks such as domain adaptation. Code is available at: https://github.com/NVlabs/ASG.

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Wuyang Chen, Zhiding Yu, Zhangyang Wang, Anima Anandkumar. 2020-07-14. Automated Synthetic-to-Real Generalization. https://arxiv.org/abs/2007.06965

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