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Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
[article]
2020
arXiv
pre-print
Few-shot classification tends to struggle when it needs to adapt to diverse domains. Due to the non-overlapping label space between domains, the performance of conventional domain adaptation is limited. Previous work tackles the problem in a transductive manner, by assuming access to the full set of test data, which is too restrictive for many real-world applications. In this paper, we set out to tackle this issue by introducing a inductive framework, DaFeC, to improve Domain adaptation
arXiv:2006.12816v1
fatcat:duwegr3z45ennfb3j6zk5zwdji