\section{Related Work}
\label{sec:relatedwork}

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% related work ==> 1.5-2 columns (4-5)


\paragraph{Contrastive learning for paired data. }

SimCLR
CLIP~\cite{radford2021learning}
XDC~\cite{alwassel2020self}
WCL~\cite{zheng2021weakly}
SupCon

FactorCL~\cite{liang2024factorized}

None of these look at unpaired contrastive learning but just think about 

\paragraph{Contrastive learning for weakly-paired data. }
Most of the prior works on contrastive learning for weakly-paired data focused on how to align between different modalities.
%WCL

\paragraph{Multimodal learning for biological datasets. } All of the datasets are not paired however, they assume the samples are paired.
With the advancement of measuring technology, ST shows up and paired data are being generated by ST and researchers started to explore multi-modality learning on paired ST dataset. However, these samples are still in early phases, cannot generate that many samples comparing to the normal sequencing techniques and most of the collected data are by single modality.

% paul review paper, alignment section
% alignment, optimal transport


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