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Updated: Aug 4, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Asymmetric Transfer Hashing With Adaptive Bipartite Graph Learning.

Jianglin Lu, Jie Zhou, Yudong Chen

    IEEE Transactions on Cybernetics
    |April 5, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel asymmetric transfer hashing (ATH) framework to solve the generalized image transfer retrieval (GITR) problem. ATH effectively bridges domain gaps for improved cross-domain visual retrieval.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Learning to hash is crucial for visual retrieval due to efficiency.
    • Existing methods fail in heterogeneous cross-domain retrieval.
    • Cross-domain retrieval faces domain and feature gaps.

    Purpose of the Study:

    • Propose a generalized image transfer retrieval (GITR) problem.
    • Introduce an asymmetric transfer hashing (ATH) framework to address GITR.
    • Develop unsupervised, semi-supervised, and supervised versions of ATH.

    Main Methods:

    • ATH characterizes domain gaps via asymmetric hash functions.
    • Minimizes feature gaps using an adaptive bipartite graph.
    • Preserves single-domain data structure with a domain affinity graph.

    Main Results:

    • ATH achieves effective knowledge transfer across domains.
    • Avoids information loss during feature alignment.
    • Demonstrates superior performance over state-of-the-art methods.

    Conclusions:

    • The proposed ATH framework effectively addresses the challenges of GITR.
    • ATH offers a robust solution for heterogeneous cross-domain visual retrieval.
    • The method shows significant improvements on various benchmarks.