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Related Experiment Video

Updated: Jan 9, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
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Rethinking Decoupled Knowledge Distillation: A Predictive Distribution Perspective.

Bowen Zheng, Ran Cheng

    IEEE Transactions on Neural Networks and Learning Systems
    |December 9, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Generalized knowledge distillation (GDKD) enhances decoupled knowledge distillation (DKD) by rethinking logit decoupling from a predictive distribution viewpoint. GDKD improves knowledge extraction by partitioning top logits and focusing on nontop logits, outperforming existing methods.

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
    08:05

    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

    Published on: January 5, 2018

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Knowledge distillation (KD) has evolved from logit-based to feature-based methods.
    • Decoupled KD (DKD) reintroduced logit knowledge with advanced strategies.
    • Deeper exploration of DKD mechanisms is needed.

    Purpose of the Study:

    • To rethink DKD from a predictive distribution perspective.
    • To introduce an enhanced generalized DKD (GDKD) loss for versatile logit decoupling.
    • To improve knowledge extraction by analyzing teacher model's predictive distribution impacts.

    Main Methods:

    • Developed generalized DKD (GDKD) loss.
    • Analyzed teacher model's predictive distribution and its impact on GDKD loss gradients.
    • Proposed a streamlined GDKD algorithm with an efficient partition strategy for multimodality.

    Main Results:

    • Identified that top logit partitioning improves nontop logit interrelationships.
    • Found that amplifying distillation loss on nontop logits enhances knowledge extraction.
    • GDKD demonstrated superior performance across multiple benchmarks (CIFAR-100, ImageNet, etc.) compared to DKD and other KD methods.

    Conclusions:

    • GDKD offers a more versatile and effective approach to knowledge distillation.
    • Understanding predictive distributions is crucial for optimizing KD.
    • The proposed GDKD algorithm achieves state-of-the-art results in knowledge distillation.