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Transfer learning data adaptation using conflation of low-level textural features
  • Raphael Wanjiku,
  • Lawrence Nderu,
  • Michael Kimwele
Raphael Wanjiku
Jomo Kenyatta University of Agriculture and Technology

Corresponding Author:[email protected]

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Lawrence Nderu
Jomo Kenyatta University of Agriculture and Technology
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Michael Kimwele
Jomo Kenyatta University of Agriculture and Technology
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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
16 Aug 2022Submitted to Engineering Reports
17 Aug 2022Submission Checks Completed
17 Aug 2022Assigned to Editor
19 Aug 2022Reviewer(s) Assigned
08 Sep 2022Editorial Decision: Revise Major
31 Oct 20221st Revision Received
01 Nov 2022Submission Checks Completed
01 Nov 2022Assigned to Editor
01 Nov 2022Review(s) Completed, Editorial Evaluation Pending
03 Nov 2022Reviewer(s) Assigned
19 Nov 2022Editorial Decision: Accept
08 Dec 2022Published in Engineering Reports. 10.1002/eng2.12603