  Partial Adaptation (PDA) addresses a practical scenario in which the target
domain contains only a subset of classes in the source domain. While PDA should
take into account both class-level and sample-level to mitigate negative
transfer, current approaches mostly rely on only one of them. In this paper, we
propose a novel approach to fully exploit multi-level associations that can
arise in PDA. Our Associative Partial Domain Adaptation (APDA) utilizes
intra-domain association to actively select out non-trivial anomaly samples in
each source-private class that sample-level weighting cannot handle.
Additionally, our method considers inter-domain association to encourage
positive transfer by mapping between nearby target samples and source samples
with high label-commonness. For this, we exploit feature propagation in a
proposed label space consisting of source ground-truth labels and target
probabilistic labels. We further propose a geometric guidance loss based on the
label commonness of each source class to encourage positive transfer. Our APDA
consistently achieves state-of-the-art performance across public datasets.
