Sequential Learning for Domain Generalization

• URL: http://arxiv.org/abs/2004.01377v1
• Date: Fri, 3 Apr 2020 05:10:33 GMT
• Title: Sequential Learning for Domain Generalization
• Authors: Da Li, Yongxin Yang, Yi-Zhe Song and Timothy Hospedales
• Abstract summary: We propose a sequential learning framework for Domain Generalization (DG) We focus on its application to the recently proposed Meta-Learning Domain generalization (MLDG)
• Score: 81.70387860425855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we propose a sequential learning framework for Domain Generalization (DG), the problem of training a model that is robust to domain shift by design. Various DG approaches have been proposed with different motivating intuitions, but they typically optimize for a single step of domain generalization -- training on one set of domains and generalizing to one other. Our sequential learning is inspired by the idea lifelong learning, where accumulated experience means that learning the $n^{th}$ thing becomes easier than the $1^{st}$ thing. In DG this means encountering a sequence of domains and at each step training to maximise performance on the next domain. The performance at domain $n$ then depends on the previous $n-1$ learning problems. Thus backpropagating through the sequence means optimizing performance not just for the next domain, but all following domains. Training on all such sequences of domains provides dramatically more `practice' for a base DG learner compared to existing approaches, thus improving performance on a true testing domain. This strategy can be instantiated for different base DG algorithms, but we focus on its application to the recently proposed Meta-Learning Domain generalization (MLDG). We show that for MLDG it leads to a simple to implement and fast algorithm that provides consistent performance improvement on a variety of DG benchmarks.

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