  One crucial aspect of partial domain adaptation (PDA) is how to select the
relevant source samples in the shared classes for knowledge transfer. Previous
PDA methods tackle this problem by re-weighting the source samples based on
their high-level information (deep features). However, since the domain shift
between source and target domains, only using the deep features for sample
selection is defective. We argue that it is more reasonable to additionally
exploit the pixel-level information for PDA problem, as the appearance
difference between outlier source classes and target classes is significantly
large. In this paper, we propose a reinforced transfer network (RTNet), which
utilizes both high-level and pixel-level information for PDA problem. Our RTNet
is composed of a reinforced data selector (RDS) based on reinforcement learning
(RL), which filters out the outlier source samples, and a domain adaptation
model which minimizes the domain discrepancy in the shared label space.
Specifically, in the RDS, we design a novel reward based on the reconstruct
errors of selected source samples on the target generator, which introduces the
pixel-level information to guide the learning of RDS. Besides, we develope a
state containing high-level information, which used by the RDS for sample
selection. The proposed RDS is a general module, which can be easily integrated
into existing DA models to make them fit the PDA situation. Extensive
experiments indicate that RTNet can achieve state-of-the-art performance for
PDA tasks on several benchmark datasets.
