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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 1551–1600 of 4240 papers

TitleStatusHype
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation—0
Enhancing CTC-Based Visual Speech Recognition—0
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging—0
Feature Adversarial Distillation for Point Cloud Classification—0
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
Feature-Align Network with Knowledge Distillation for Efficient Denoising—0
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution—0
Feature-based One-For-All: A Universal Framework for Heterogeneous Knowledge Distillation—0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning—0
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning—0
Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation—0
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
Feature Kernel Distillation—0
Enhancing Chinese Multi-Label Text Classification Performance with Response-based Knowledge Distillation—0
A Technical Study into Small Reasoning Language Models—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials—0
Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification—0
Feature Structure Distillation for BERT Transferring—0
Enhancing Adversarial Training with Prior Knowledge Distillation for Robust Image Compression—0
Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold—0
Compressing GANs using Knowledge Distillation—0
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning—0
CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone—0
GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation—0
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization—0
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation—0
FedDKD: Federated Learning with Decentralized Knowledge Distillation—0
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction—0
FedEFM: Federated Endovascular Foundation Model with Unseen Data—0
Federated Action Recognition on Heterogeneous Embedded Devices—0
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process—0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization—0
Compressing Deep Image Super-resolution Models—0
Gradient Adversarial Training of Neural Networks—0
Enhanced Sparsification via Stimulative Training—0
Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions—0
Federated Graph Learning with Graphless Clients—0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction—0
Enhanced Multimodal Representation Learning with Cross-modal KD—0
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients—0
Federated Learning for Data and Model Heterogeneity in Medical Imaging—0
Compressed Meta-Optical Encoder for Image Classification—0
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All datasetsImageNetCIFAR-100COCO (Common Objects in Context)COCO 2017 valPASCAL VOCKITTI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ScaleKD (T:BEiT-L S:ViT-B/14)Top-1 accuracy %86.43—Unverified
2ScaleKD (T:Swin-L S:ViT-B/16)Top-1 accuracy %85.53—Unverified
3ScaleKD (T:Swin-L S:ViT-S/16)Top-1 accuracy %83.93—Unverified
4ScaleKD (T:Swin-L S:Swin-T)Top-1 accuracy %83.8—Unverified
5KD++(T: regnety-16GF S:ViT-B)Top-1 accuracy %83.6—Unverified
6VkD (T:RegNety 160 S:DeiT-S)Top-1 accuracy %82.9—Unverified
7SpectralKD (T:Swin-S S:Swin-T)Top-1 accuracy %82.7—Unverified
8ScaleKD (T:Swin-L S:ResNet-50)Top-1 accuracy %82.55—Unverified
9DiffKD (T:Swin-L S: Swin-T)Top-1 accuracy %82.5—Unverified
10DIST (T: Swin-L S: Swin-T)Top-1 accuracy %82.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SRD (T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)79.86—Unverified
2shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)78.76—Unverified
3MV-MR (T: CLIP/ViT-B-16 S: resnet50)Top-1 Accuracy (%)78.6—Unverified
4resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)78.28—Unverified
5resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])Top-1 Accuracy (%)78.08—Unverified
6ReviewKD++(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)77.93—Unverified
7ReviewKD++(T:resnet-32x4, S:shufflenet-v1)Top-1 Accuracy (%)77.68—Unverified
8resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)77.5—Unverified
9resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.68—Unverified
10resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.31—Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP77.16—Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP73.73—Unverified
3ADLIK-Faster (T: Faster R-CNN vit-base S: Faster R-CNN deit-small) box AP47.6—Unverified
4ADLIK-Mask (T: Mask R-CNN vit-base S: Mask R-CNN deit-small)mask AP42.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(resnet50))AP@0.561.8—Unverified
2ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(resnet18))AP@0.557.96—Unverified
3ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(mobilenet-v2))AP@0.555.18—Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP93.17—Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP90.14—Unverified
#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43—Unverified