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Deep Neural Networks for Image-Based Dietary Assessment
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Hanbin Zhao, Hui Wang, Yongjian Fu

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    This study introduces a novel approach to class-incremental learning (CIL) by using low-fidelity exemplar samples to combat catastrophic forgetting. This method enhances memory efficiency and knowledge transfer in CIL models.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Class-incremental learning (CIL) faces challenges with catastrophic forgetting due to memory and resource constraints.
    • Existing CIL methods often rely on preserving exemplar samples of old classes in a limited memory buffer.

    Purpose of the Study:

    • To develop a memory-efficient exemplar preservation scheme for CIL.
    • To address the domain shift issue associated with using low-fidelity exemplar samples.
    • To improve the effectiveness of knowledge transfer from old to new classes in CIL.

    Main Methods:

    • Proposing a scheme to preserve more auxiliary low-fidelity exemplar samples instead of high-fidelity ones.
    • Introducing a duplet learning scheme to create domain-compatible feature extractors and classifiers, reducing domain gap.
    • Presenting a robust classifier adaptation scheme to refine classifiers using pure true class labels.

    Main Results:

    • Low-fidelity auxiliary exemplar samples effectively replace original samples with reduced memory cost.
    • The duplet learning and classifier adaptation schemes significantly narrow the domain gap.
    • The proposed methods demonstrate superior performance against state-of-the-art approaches in CIL.

    Conclusions:

    • The novel exemplar preservation and learning schemes effectively mitigate catastrophic forgetting in CIL.
    • This approach offers a more memory-efficient and effective solution for incremental learning tasks.
    • The research facilitates future advancements in CIL with released code and data.