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Class-Incremental Unsupervised Domain Adaptation via Pseudo-Label Distillation.

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    This study introduces Pseudo-Label Distillation Continual Adaptation (PLDCA) to improve Class-Incremental Unsupervised Domain Adaptation (CI-UDA). PLDCA effectively filters biased source knowledge and aligns domain features for better continual learning performance.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Class-Incremental Unsupervised Domain Adaptation (CI-UDA) presents challenges in continually learning from unlabeled target domains while retaining source domain knowledge.
    • Existing CI-UDA methods often suffer from negative transfer due to biased source knowledge, leading to suboptimal performance.
    • Effective domain-invariant knowledge transfer and preservation of past learning are crucial for successful CI-UDA.

    Purpose of the Study:

    • To propose a novel CI-UDA method, Pseudo-Label Distillation Continual Adaptation (PLDCA), to address the limitations of existing approaches.
    • To mitigate negative transfer by filtering biased source knowledge using target domain information.
    • To enhance feature representation alignment between source and target domains for improved continual adaptation.

    Main Methods:

    • PLDCA utilizes a Pseudo-Label Distillation module to filter biased source knowledge at both class and instance levels, leveraging target domain discriminative information.
    • Contrastive Alignment is employed to reduce domain discrepancy by aligning class-level features of confident target samples with the source domain.
    • Instance-level feature representation of unconfident target samples is exploited for robust learning.

    Main Results:

    • Extensive experiments validate the effectiveness of the proposed PLDCA method.
    • PLDCA demonstrates superior performance compared to existing CI-UDA techniques.
    • The method successfully addresses issues of biased knowledge transfer and domain discrepancy.

    Conclusions:

    • PLDCA offers a significant advancement in Class-Incremental Unsupervised Domain Adaptation.
    • The proposed modules effectively filter biased knowledge and align domain features, leading to improved continual learning.
    • The method shows strong potential for real-world applications requiring continual domain adaptation.