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Knowledge Distillation

Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.

Papers

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Showing 1–10 of 4240 papers

TitleStatusHype
Visual-Language Model Knowledge Distillation Method for Image Quality Assessment—0
Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces—0
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action RecognitionCode0
HanjaBridge: Resolving Semantic Ambiguity in Korean LLMs via Hanja-Augmented Pre-Training—0
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning—0
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift—0
SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation—0
KAT-V1: Kwai-AutoThink Technical Report—0
The Trilemma of Truth in Large Language ModelsCode0
Layer Importance for Mathematical Reasoning is Forged in Pre-Training and Invariant after Post-Training—0
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All datasetsImageNetCIFAR-100COCO (Common Objects in Context)COCO 2017 valPASCAL VOCKITTI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43—Unverified