Abstract
Recent breakthroughs in generative neural networks have paved the way for transformative capabilities, particularly in their capacity to generate novel data, notably in the realm of images. The integration of these models with the increasingly popular technique of transfer learning, designed for proficient feature extraction, holds the promise of enhancing overall performance. This paper delves into the exploration of employing generative models in conjunction with transfer learning methods for feature extraction, with a specific focus on image classification tasks. Our investigation aims to scrutinize the effectiveness of leveraging generative models alongside pre-trained models as feature extractors in the context of image classification. To the best of our knowledge, our investigation is the first to link transfer learning and generative models for a discriminative task under one roof. The proposed approach undergoes rigorous evaluation on two distinct datasets, employing specific metrics to gauge the model’s performance. The results exhibit a notable nearly 10% enhancement achieved through the integration of generative models, underscoring their potential for achieving heightened accuracy in image classification. These findings highlight significant advancements in image classification accuracy, surpassing the performance of conventional Artificial Neural Network (ANN) models.
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Conceptualization, D.E. and Y.M.B; methodology, Y.M.B., D.E., and S.S.; software, D.E.; validation, D.E. and Y.M.B.; formal analysis, Y.M.B. and D.E.; resources, S.S., D.E., and Y.M.B.; data curation, Y.M.B., D.E.; writing-original draft preparation, D.E.; writing-review and editing, D.E. and Y.M.B.; supervision, Y.M.B. and S.S.; project administration, Y.M.B. and S.S.; funding acquisition, Y.M.B. All authors have read and agreed to the published version of the manuscript.
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Ebrahimzadeh, D., Sharif, S. & Banad, Y. How transfer learning is used in generative models for image classification: improved accuracy. SIViP 19, 103 (2025). https://doi.org/10.1007/s11760-024-03673-5
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DOI: https://doi.org/10.1007/s11760-024-03673-5