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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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Updated: Sep 19, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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CKDF-V2: Effectively Alleviating Representation Shift for Continual Learning With Small Memory.

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    Summary
    This summary is machine-generated.

    Continual learning (CL) models face catastrophic forgetting due to representation shift. Feature boosting calibration (FBC) and blockwise knowledge distillation (BWKD) in CKDF-V2 enhance feature transferability and resolve data imbalance for improved CL performance.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Continual learning (CL) models struggle with catastrophic forgetting when encountering out-of-distribution data.
    • Representation shift (RS) occurs during model updates, degrading performance on previously learned tasks.
    • Limited memory in CL exacerbates data imbalance between old and new classes.

    Purpose of the Study:

    • To propose novel methods, Feature Boosting Calibration (FBC) and Blockwise Knowledge Distillation (BWKD), to mitigate catastrophic forgetting and representation shift in continual learning.
    • To introduce CKDF-V2, a two-stage training framework integrating FBC and BWKD for enhanced continual learning.
    • To adapt CKDF-V2 for Vision Transformers (ViT) using task-token expansion.

    Main Methods:

    • Feature Boosting Calibration (FBC): An expanded module identifies and utilizes missed critical features to calibrate old representations, enhancing feature transferability.
    • Blockwise Knowledge Distillation (BWKD): The softmax layer is split into blocks based on class frequency for separate distillation, effectively addressing data imbalance.
    • CKDF-V2 Framework: A two-stage training approach combining FBC and BWKD, with an extension for ViT integration using task-token expansion.

    Main Results:

    • FBC successfully calibrates representations by exploiting missed features, thereby alleviating representation shift.
    • BWKD effectively resolves data imbalance issues inherent in continual learning scenarios.
    • The proposed CKDF-V2 framework, applied to both CNNs and ViTs, achieved favorable results across multiple continual learning benchmarks.

    Conclusions:

    • CKDF-V2, incorporating FBC and BWKD, offers a robust solution for continual learning challenges, significantly improving model performance.
    • The integration with ViT demonstrates the framework's versatility and effectiveness in modern deep learning architectures.
    • The proposed methods effectively combat catastrophic forgetting and representation shift, paving the way for more stable continual learning systems.