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Transfer Hashing: From Shallow to Deep.

Joey Tianyi Zhou, Heng Zhao, Xi Peng

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    Summary
    This summary is machine-generated.

    This study introduces transfer hashing with privileged information (THPI) to address data sparsity in hashing. THPI effectively leverages source domain data for improved target domain hashing performance, demonstrated by novel ITQ variants and deep transfer hashing.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Mining

    Background:

    • Hashing methods often assume sufficient training data in the target domain, which is not always practical.
    • Data sparsity in the target domain poses a significant challenge for traditional hashing techniques.

    Purpose of the Study:

    • To propose a novel framework, transfer hashing with privileged information (THPI), to overcome data sparsity in hashing.
    • To demonstrate the effectiveness of THPI through practical implementations and comparisons with existing methods.

    Main Methods:

    • Developed three variants of iterative quantization (ITQ) within the THPI framework: ITQ+, LapITQ+, and deep transfer hashing (DTH).
    • ITQ+ adapts ITQ for transfer learning by learning a slack function from a source domain.
    • LapITQ+ enhances ITQ+ by incorporating geometric relationships from the source domain.
    • DTH utilizes deep learning for robust hashing, showcasing the framework's generality.

    Main Results:

    • Extensive experiments on multiple datasets validated the effectiveness of the proposed shallow (ITQ+, LapITQ+) and deep (DTH) transfer hashing methods.
    • The proposed approaches significantly outperform several state-of-the-art hashing techniques in addressing data sparsity.

    Conclusions:

    • The THPI framework offers a viable solution to the data sparsity problem in hashing.
    • Both shallow and deep learning-based transfer hashing methods show promising results, advancing the field of efficient data retrieval.