Multi-Source Transfer Learning Based on Fuzzy Rules for Improving Image Classification Accuracy

Image classification tasks often involve the challenge of acquiring a sufficient number of labeled training samples, a process that is not only expensive but also time-consuming. In response to this issue, researchers have focused on transfer learning algorithms, which capitalize on prior knowledge to enhance a model training. While numerous existing transfer learning methods concentrate on knowledge transfer between a single-source domain and a single-target domain, the complexity of real-world scenarios is often underestimated. Limited studies have delved into domain adaptation within multi-source environments, where transferring knowledge from multiple sources introduces ambiguity and uncertainty into the learning process. To address this challenge, this study proposes the application of fuzzy rule-based transfer learning, leveraging the inherent ability of fuzzy rules to effectively handle uncertainty. One notable aspect of fuzzy transfer learning, and transfer learning in general, is the unresolved question of effectively combining and utilizing knowledge when multiple source domains are available. This issue is particularly pertinent in scenarios involving diverse datasets from various sources. Consequently, the present study introduces a novel approach to multi-source transfer learning anchored in fuzzy rules. By integrating fuzzy logic, the proposed method aims to provide a robust solution to the challenges posed by knowledge transfer in scenarios with multiple source domains. This research contributes to advancing transfer learning methodologies, offering a nuanced perspective on handling uncertainty in multi-source environments by applying fuzzy rule-based techniques. In conclusion, the significance of transfer learning in image classification tasks is underscored by the inherent challenges of acquiring labeled training data. The conventional focus on single-source to single-target domain transfer has limitations, prompting a shift towards addressing the more realistic and challenging scenarios of multi-source domain adaptation. This study introduces a pioneering approach to multi-source transfer learning, utilizing fuzzy rule-based techniques to effectively navigate the complexities introduced by knowledge transfer from multiple sources. Through this contribution, the research aims to propel advancements in transfer learning methodologies and foster a more comprehensive understanding of handling uncertainty in multi-source environments.