Be Reliable: An Interpretable Attribute-Oriented Representation Learning Framework

Representation learning techniques effectively unveil latent patterns within raw data. However, the learning process is often marred by uncertainties, such as variations in data quality and heterogeneous scenarios, which greatly affect the reliability of representation learning. In this article, we introduce a reliable representation learning framework to establish a connection between data attributes and modeling strategies, namely the interpretable attribute-oriented representation learning framework. First, by focusing on the inherent knowledge embedded in the data, we decouple it into four principal attributes: fidelity, topology, invariance, and discriminability. To explicitly address these attributes, we incorporate them into an optimization-derived framework using corresponding general loss terms. Furthermore, by treating the iterative solution process as a bridge, each derived network module possesses traceable interpretability, thus laying a reliable foundation. Ultimately, we extend the proposed framework to multisource heterogeneous scenarios, enabling it to adapt to complex environments while maintaining reliability. In essence, our work aims to seamlessly integrate deep representations with prior knowledge during the learning process, thereby creating a solid basis for dependable modeling. Networks derived from the proposed framework achieve promising results, particularly in complex multisource heterogeneous environments, demonstrating both their effectiveness and reliability. The code is available at https://github.com/ZihanFang11/2025_AORLNet_TNNLS.