most citedLearning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment

5 citations · 5 across the 1 of their papers we have counts for

collaborators

1 paper

cs.LG2019★ 5 cited

Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment

Alex Gabourie, Mohammad Rostami, Philip Pope +2

We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the…