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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
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Disentangled Noisy Correspondence Learning.

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    This study introduces DisNCL, a framework for feature disentanglement in noisy correspondence learning. DisNCL improves cross-modal retrieval accuracy by adaptively balancing modality-invariant and exclusive information, achieving a 2% average recall improvement.

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    Area of Science:

    • Artificial Intelligence
    • Computer Science
    • Information Theory

    Background:

    • Cross-modal retrieval relies on understanding relationships between different data types.
    • Real-world data often has imperfect alignments (noisy correspondences), hindering retrieval accuracy.
    • Existing methods struggle with modality-exclusive information (MEI) and noise tolerance.

    Purpose of the Study:

    • To develop a robust framework for feature disentanglement in noisy cross-modal learning.
    • To enhance similarity predictions by focusing on modality-invariant information (MII).
    • To improve cross-modal alignment accuracy despite inherent data noise.

    Main Methods:

    • Introduced DisNCL, an information-theoretic framework for feature disentanglement.
    • Adaptively balanced extraction of MII and MEI using information bottlenecks.
    • Enhanced similarity predictions in a modality-invariant subspace.
    • Employed soft matching targets for noisy many-to-many relationships.

    Main Results:

    • Achieved a 2% average recall improvement in cross-modal retrieval.
    • Demonstrated effective learning of meaningful MII and MEI subspaces via mutual information estimation.
    • Validated the framework's robustness and efficacy in handling noisy correspondences.

    Conclusions:

    • DisNCL offers a certifiably optimal approach to cross-modal disentanglement.
    • The framework significantly boosts similarity-based strategies for noisy data.
    • DisNCL provides noise-robust and accurate cross-modal alignment for multi-modal inputs.