Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain
- URL: http://arxiv.org/abs/2209.01870v1
- Date: Mon, 5 Sep 2022 10:06:03 GMT
- Title: Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain
- Authors: Lianyu Wang, Meng Wang, Daoqiang Zhang and Huazhu Fu
- Abstract summary: Unsupervised domain adaptation (UDA) has attracted considerable attention, which transfers knowledge from a label-rich source domain to a related but unlabeled target domain.
We propose a novel style-aware feature fusion method (SAFF) to bridge the large domain gap and transfer knowledge while alleviating the loss of class-discnative information.
- Score: 52.08301776698373
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Unsupervised domain adaptation (UDA) has attracted considerable attention,
which transfers knowledge from a label-rich source domain to a related but
unlabeled target domain. Reducing inter-domain differences has always been a
crucial factor to improve performance in UDA, especially for tasks where there
is a large gap between source and target domains. To this end, we propose a
novel style-aware feature fusion method (SAFF) to bridge the large domain gap
and transfer knowledge while alleviating the loss of class-discriminative
information. Inspired by the human transitive inference and learning ability, a
novel style-aware self-intermediate domain (SSID) is investigated to link two
seemingly unrelated concepts through a series of intermediate auxiliary
synthesized concepts. Specifically, we propose a novel learning strategy of
SSID, which selects samples from both source and target domains as anchors, and
then randomly fuses the object and style features of these anchors to generate
labeled and style-rich intermediate auxiliary features for knowledge transfer.
Moreover, we design an external memory bank to store and update specified
labeled features to obtain stable class features and class-wise style features.
Based on the proposed memory bank, the intra- and inter-domain loss functions
are designed to improve the class recognition ability and feature
compatibility, respectively. Meanwhile, we simulate the rich latent feature
space of SSID by infinite sampling and the convergence of the loss function by
mathematical theory. Finally, we conduct comprehensive experiments on commonly
used domain adaptive benchmarks to evaluate the proposed SAFF, and the
experimental results show that the proposed SAFF can be easily combined with
different backbone networks and obtain better performance as a plug-in-plug-out
module.
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