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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. Considering the inherent conditional and label shifts, we would expect the alignment of p(x|y) and p(y). However, the widely used domain invariant feature learning (IFL) methods relies on aligning the marginal concept shift w.r.t. p(x), which rests on an unrealistic assumption that p(y) is invariant across domains.doi:10.24963/ijcai.2021/122 fatcat:drgwae5wcrhntd3winh4qdii3y