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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Self-supervised learning (SSL) aims to learn representations without labeled data.
    • Feature collapse is a key challenge in SSL, where different inputs map to identical representations.
    • Whitening loss is a technique to prevent feature collapse by ensuring whitened embeddings.

    Purpose of the Study:

    • To analyze whitening loss and its role in preventing feature collapse in SSL.
    • To demystify phenomena related to whitening loss and connect it to other SSL methods.
    • To propose a novel method that overcomes limitations of existing whitening techniques.

    Main Methods:

    • Developed an analytical framework with an indicator to study whitening loss.
    • Demonstrated that batch whitening (BW) requires full-rank embeddings, not strict whitening.
    • Proposed Channel Whitening with Random Group Partition (CW-RGP) based on analysis.

    Main Results:

    • Showed that full-rank constraint is sufficient to avoid dimensional collapse.
    • Proved stable rank invariance during gradient descent under specific conditions.
    • CW-RGP effectively prevents collapse without requiring large batch sizes.

    Conclusions:

    • The proposed CW-RGP method leverages advantages of BW while mitigating its drawbacks.
    • CW-RGP demonstrates significant potential for learning high-quality representations.
    • The analysis provides insights into whitening loss and its connection to other SSL approaches.