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\author[1]{\href{mailto:Tuan Nguyen <tuan.ng@monash.edu>?Subject=Paper "Cycle Class Consistency with Distributional Optimal Transport and Knowledge Distillation for Unsupervised Domain Adaptation"
}{Tuan Nguyen}{}} % Lead author
\author[2]{Van Nguyen}
\author[1]{Trung Le}
\author[1]{He Zhao}
\author[3]{Quan Hung Tran}
\author[1,4]{Dinh Phung}
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\affil[1]{%
    Department of Data Science and AI\\
    Monash University\\
    Australia
}

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    The University of Adelaide\\
    Australia
}

\affil[3]{%
    Adobe Research\\
    San Jose, CA, USA
}

\affil[4]{%
    VinAI Research\\
    Vietnam
}

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\begin{document}
\title{Cycle Class Consistency with Distributional Optimal Transport\\and
Knowledge Distillation for Unsupervised Domain Adaptation}
\maketitle
\begin{abstract}
Unsupervised domain adaptation (UDA) aims to transfer knowledge from
a model trained on a labeled source domain to an unlabeled target
domain. To this end, we propose in this paper a novel cycle class-consistent
model based on optimal transport (OT) and knowledge distillation.
The model consists of two agents, a teacher and a student cooperatively
working in a cycle process under the guidance of the distributional
optimal transport and distillation manner. The OT distance is designed
to bridge the gap between the distribution of the target data and
a distribution over the source class-conditional distributions. The
optimal probability matrix then provides pseudo labels to learn a
teacher that achieves a good classification performance on the target
domain. Knowledge distillation is performed in the next step in which
the teacher distills and transfers its knowledge to the student. And
finally, the student produces its prediction for the optimal transport
step. This process forms a closed cycle in which the teacher and student
networks are simultaneously trained to conduct transfer learning from
the source to the target domain. Extensive experiments show that our
proposed method outperforms existing methods, especially the class-aware
and OT-based ones on benchmark datasets including Office-31, Office-Home,
and ImageCLEF-DA.

\input{macros.tex}
\end{abstract}

\section{Introduction}

\input{introduction.tex}

\section{Related Work}

\input{related_work.tex}

\section{Background}

\input{background.tex}

\section{Distributional Optimal Transport Approach for Class-Aware UDA}

\input{distributional_ot.tex}

\section{Cycle Class Consistency Framework}

\input{our_approach.tex}

\section{Experiments}

\input{experiment.tex}

\section{Conclusion}

In this paper, we develop a novel framework for class-aware unsupervised
domain adaptation. In particular, our proposed method is based on
the proposed distributional OT which quantifies an OT distance between
a distribution of target data and the source class-conditional distributions.
To efficiently train our model with the proposed distributional OT,
we develop a novel model operating in a cyclic process. By incorporating
knowledge distillation and pseudo labelling technique into this process,
our proposed COOK effectively tackles the data and label shifts problem
by transporting the target samples to the corresponding source class-conditional
distributions in a class-aware manner. The experimental results show
that COOK outperforms existing UDA methods, especially the class-aware
and OT-based ones on the benchmark datasets.

\noindent \begin{acknowledgements}

\noindent This work was supported by the US Air Force grant FA2386-21-1-4049. 

\noindent \end{acknowledgements}

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