  Transfer learning leverages knowledge from other domains and has been
successful in many applications. Transfer learning methods rely on the overall
similarity of the source and target domains. However, in some cases, it is
impossible to provide an overall similar source domain, and only some source
domains with similar local features can be provided. Can transfer learning be
achieved? In this regard, we propose a multi-source adversarial transfer
learning method based on local feature similarity to the source domain to
handle transfer scenarios where the source and target domains have only local
similarities. This method extracts transferable local features between a single
source domain and the target domain through a sub-network. Specifically, the
feature extractor of the sub-network is induced by the domain discriminator to
learn transferable knowledge between the source domain and the target domain.
The extracted features are then weighted by an attention module to suppress
non-transferable local features while enhancing transferable local features. In
order to ensure that the data from the target domain in different sub-networks
in the same batch is exactly the same, we designed a multi-source domain
independent strategy to provide the possibility for later local feature fusion
to complete the key features required. In order to verify the effectiveness of
the method, we made the dataset "Local Carvana Image Masking Dataset". Applying
the proposed method to the image segmentation task of the proposed dataset
achieves better transfer performance than other multi-source transfer learning
methods. It is shown that the designed transfer learning method is feasible for
transfer scenarios where the source and target domains have only local
similarities.
