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Poster
Automated Synthetic-to-Real Generalization
Wuyang Chen · Zhiding Yu · Zhangyang “Atlas” Wang · Anima Anandkumar

Wed Jul 15 04:00 PM -- 04:45 PM & Thu Jul 16 04:00 AM -- 04:45 AM (PDT) @
in Poster Session 30 »

Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pretrained 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 pretrained 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.

Keywords: Transfer and Multitask Learning · Meta-learning and Automated ML · Transfer, Multitask and Meta-learning

Author Information

Wuyang Chen (Texas A&M University)
Zhiding Yu (NVIDIA)

Zhiding Yu is a Senior Research Scientist at NVIDIA. Before joining NVIDIA in 2018, he received Ph.D. in ECE from Carnegie Mellon University in 2017, and M.Phil. in ECE from The Hong Kong University of Science and Technology in 2012. His research interests mainly focus on deep representation learning, weakly/self-supervised learning, transfer learning and deep structured prediction, with their applications to vision and robotics problems.

Zhangyang “Atlas” Wang (University of Texas at Austin)
Anima Anandkumar (Amazon AI & Caltech)

Anima Anandkumar is a Bren Professor at Caltech and Director of ML Research at NVIDIA. She was previously a Principal Scientist at Amazon Web Services. She is passionate about designing principled AI algorithms and applying them to interdisciplinary domains. She has received several honors such as the IEEE fellowship, Alfred. P. Sloan Fellowship, NSF Career Award, Young investigator awards from DoD, Venturebeat’s “women in AI” award, NYTimes GoodTech award, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network. She has appeared in the PBS Frontline documentary on the “Amazon empire” and has given keynotes in many forums such as the TEDx, KDD, ICLR, and ACM. Anima received her BTech from Indian Institute of Technology Madras, her PhD from Cornell University, and did her postdoctoral research at MIT and assistant professorship at University of California Irvine.

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