Adversarial Teacher-Student Representation Learning for Domain GeneralizationDownload PDF

21 May 2021, 20:42 (edited 21 Jan 2022)NeurIPS 2021 SpotlightReaders: Everyone
  • Keywords: Domain Generalization, Representation Learning, Adversarial Learning
  • TL;DR: For DG problems, our proposed adversarial learning framework learns the domain generalizable representations via generating and exploring out-of-source data distributions.
  • Abstract: Domain generalization (DG) aims to transfer the learning task from a single or multiple source domains to unseen target domains. To extract and leverage the information which exhibits sufficient generalization ability, we propose a simple yet effective approach of Adversarial Teacher-Student Representation Learning, with the goal of deriving the domain generalizable representations via generating and exploring out-of-source data distributions. Our proposed framework advances Teacher-Student learning in an adversarial learning manner, which alternates between knowledge-distillation based representation learning and novel-domain data augmentation. The former progressively updates the teacher network for deriving domain-generalizable representations, while the latter synthesizes data out-of-source yet plausible distributions. Extensive image classification experiments on benchmark datasets in multiple and single source DG settings confirm that, our model exhibits sufficient generalization ability and performs favorably against state-of-the-art DG methods.
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