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Updated: Sep 8, 2025

Automatic Identification of Dendritic Branches and their Orientation
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Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning.

Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |July 8, 2025
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    Summary
    This summary is machine-generated.

    Self-supervised learning (SSL) faces challenges in continual learning (CL). We introduce branch-tuning (BT), a method balancing model stability and plasticity for efficient adaptation to new data in continual SSL.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Self-supervised learning (SSL) excels at extracting general representations from unlabeled data.
    • Continual learning (CL) is crucial for adapting SSL models to evolving real-world data without complete retraining.
    • Balancing model stability and plasticity is a key challenge in continual SSL.

    Purpose of the Study:

    • To quantitatively analyze model stability and plasticity in continual SSL.
    • To propose an efficient method for balancing stability and plasticity in continual SSL.
    • To offer insights for future research in continual SSL.

    Main Methods:

    • Employed centered kernel alignment (CKA) to analyze model stability and plasticity.
    • Identified batch normalization (BN) layers as critical for stability and convolutional layers for plasticity.
    • Proposed branch-tuning (BT), a method involving branch expansion and compression for continual SSL.

    Main Results:

    • CKA analysis revealed distinct roles of BN and convolutional layers in stability and plasticity.
    • Branch-tuning (BT) effectively balances stability and plasticity in continual SSL.
    • BT is applicable to various SSL methods without altering original models or requiring old data.

    Conclusions:

    • Branch-tuning (BT) offers an efficient and practical solution for continual self-supervised learning.
    • The proposed method demonstrates effectiveness and practical value in real-world scenarios.
    • This work provides valuable insights for advancing continual SSL research.