XCapsUTL: Cross-domain unsupervised transfer learning framework using a capsule neural network
2024
As e-commerce stores broaden their reach into new regions and introduce new products within established markets, the development of effective machine learning models becomes increasingly challenging due to the scarcity of labeled data. Traditional transfer learning methods typically require some labeled data from the target domain and often face computational bottlenecks. Despite the availability of a few transfer learning techniques, most are primarily developed for vision and text applications, making them unsuitable for other types of data. In many industries, however, tabular data is a predominant and crucial data type. Our work introduces XCap-sUTL, a novel unsupervised transfer learning framework specifically designed for tabular data, aiming to fill this significant gap. Our approach leverages Capsule Neural Networks (CapsNet) to extract domain-agnostic knowledge. This knowledge is then refined using a constrained fine-tuning process, ensuring adaptability to the target task while preserving learned representations. XCapsUTL’s unique feature encapsulation capabilities within CapsNet promote effective knowledge transfer without the need for designing effective feature-wise interaction approaches to capture higher-level semantics. Extensive experiments demonstrate the robustness and generalization capabilities of XCapsUTL across multiple domains and datasets, highlighting its practical significance and utility in addressing the unique challenges of tabular data in industry settings.
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