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\begin{document}

\title{Transfer learning data adaptation using conflation of low-level textural
features}



\author[1]{Raphael Wanjiku}%
\author[1]{Lawrence  Nderu}%
\author[1]{Michael Kimwele}%
\affil[1]{Jomo Kenyatta University of Agriculture and Technology}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
Adapting the target dataset for a pre-trained model is still
challenging. These adaptation problems result from a lack of adequate
transfer of traits from the source dataset; this often leads to poor
model performance resulting in trial and error in selecting the best
performing pre-trained model. This paper introduces the conflation of
source domain low-level textural features extracted using the first
layer of the pretrained model. The extracted features are compared to
the conflated low-level features of the target dataset to select a
higher quality target dataset for improved pre-trained model performance
and adaptation. From comparing the various probability distance metrics,
Kullback-Leibler is adopted to compare the samples from both domains. We
experiment on three publicly available datasets and two ImageNet
pre-trained models used in past studies for results comparisons. This
proposed approach method yields two categories of the target samples
with those with lower Kullback-Leibler values giving better accuracy,
precision and recall. The samples with the lower Kullback-Leibler values
give a higher margin accuracy rate of 6.21\% to 7.27\%, thereby leading
to better model adaptation for target transfer learning datasets and
tasks%
\end{abstract}%



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\textbf{Hosted file}

\verb`Transfer learning data adaptation using conflation of low-level textural features.docx`  available at \url{https://authorea.com/users/467175/articles/582169-transfer-learning-data-adaptation-using-conflation-of-low-level-textural-features}\selectlanguage{english}
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