196 research outputs found
Unsupervised Domain Adaptation with Progressive Adaptation of Subspaces
Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain
by transferring knowledge from labeled source domain with domain shift. Most of
the existing UDA methods try to mitigate the adverse impact induced by the
shift via reducing domain discrepancy. However, such approaches easily suffer a
notorious mode collapse issue due to the lack of labels in target domain.
Naturally, one of the effective ways to mitigate this issue is to reliably
estimate the pseudo labels for target domain, which itself is hard. To overcome
this, we propose a novel UDA method named Progressive Adaptation of Subspaces
approach (PAS) in which we utilize such an intuition that appears much
reasonable to gradually obtain reliable pseudo labels. Speci fically, we
progressively and steadily refine the shared subspaces as bridge of knowledge
transfer by adaptively anchoring/selecting and leveraging those target samples
with reliable pseudo labels. Subsequently, the refined subspaces can in turn
provide more reliable pseudo-labels of the target domain, making the mode
collapse highly mitigated. Our thorough evaluation demonstrates that PAS is not
only effective for common UDA, but also outperforms the state-of-the arts for
more challenging Partial Domain Adaptation (PDA) situation, where the source
label set subsumes the target one
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