DESIGN OF A TRANSFER LEARNING-BASED DEEP LEARNING MODEL FOR DOMAIN-ADAPTIVE CLASSIFICATION

The goal of developing and using a deep learning model for transfer learning-based domain- adaptive categorization is to address performance degradation that occurs when a model trained on one domain is transferred to a different but related target domain. This strategy lessens the need for huge labeled datasets in the target domain by employing a pretrained convolutional neural network (CNN) as a feature extractor to reuse previously learnt information. By aligning feature representations through domain adaptation processes, the system improves classification performance in a variety of previously untested contexts. Experiments conducted on benchmark datasets show notable advancements in accuracy and resilience to domain alterations.