  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 an
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 for partial domain adaptation
demonstrate the superiority of our proposed framework over state-of-the-art
methods.
