Transfer learning data adaptation using conflation of low‐level textural features

Abstract

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 pre‐trained 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 0.22%–9.15%, thereby leading to better model adaptation and easier model selection process for the target transfer learning datasets and tasks.

Keywords

• ImageNet pre‐trained model
• Kullback‐Leibler divergence
• textural low‐level features