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Multilevel Distribution Alignment for Multisource Universal Domain Adaptation.

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    This study introduces a new method for multisource universal domain adaptation (MSUDA) to classify data from multiple sources, even with unknown categories. The approach effectively identifies both known and unknown samples while aligning feature distributions.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Multisource universal domain adaptation (MSUDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain.
    • Key challenges include handling an arbitrary number of source domains, private (unknown) categories in the target domain, and domain discrepancies.
    • Existing methods struggle with identifying unknown target samples and extracting robust domain-invariant features.

    Purpose of the Study:

    • To propose a novel network, the multirepresentation DA network (MRDAN), for classifying unlabeled target data using multiple source domains with non-identical label sets.
    • To address the challenge of identifying both known and unknown samples in the target domain.
    • To extract domain-invariant features effectively despite distribution discrepancies across domains.

    Main Methods:

    • Introduced a threshold-free conflict-based predictions with uncertainty (CPU) module to simultaneously identify known and unknown samples by leveraging complementary knowledge from source domains.
    • Developed a multilevel distribution alignment (MLDA) strategy to progressively reduce distribution discrepancies between multiple domains with non-identical category spaces.
    • Utilized these modules within the proposed MRDAN framework for classification.

    Main Results:

    • The proposed MRDAN effectively classifies unlabeled target data by harnessing knowledge from multiple source domains.
    • The CPU module successfully identifies both known and unknown samples without requiring predefined thresholds.
    • The MLDA strategy facilitates accurate extraction of domain-invariant features, improving recognition of both known and unknown samples.

    Conclusions:

    • The MRDAN framework demonstrates significant effectiveness in multisource universal domain adaptation, particularly in scenarios with unknown target categories.
    • The combination of CPU and MLDA modules provides a robust solution for handling domain discrepancies and private categories.
    • Experimental results on three benchmark datasets validate the proposed approach's superiority in recognizing both known and unknown samples.