Partial domain adaptation which assumes that the unknown target label space
is a subset of the source label space has attracted much attention in computer
vision. Despite recent progress, existing methods often suffer from three key
problems: negative transfer, lack of discriminability and domain invariance in
the latent space. To alleviate the above issues, we develop a novel 'Select,
Label, and Mix' (SLM) framework that aims to learn discriminative invariant
feature representations for partial domain adaptation. First, we present a
simple yet efficient "select" module that automatically filters out the outlier
source samples to avoid negative transfer while aligning distributions across
both domains. Second, the "label" module iteratively trains the classifier
using both the labeled source domain data and the generated pseudo-labels for
the target domain to enhance the discriminability of the latent space. Finally,
the "mix" module utilizes domain mixup regularization jointly with the other
two modules to explore more intrinsic structures across domains leading to a
domain-invariant latent space for partial domain adaptation. Extensive
experiments on several benchmark datasets demonstrate the superiority of our
proposed framework over state-of-the-art methods