SOTAVerified

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 1351–1375 of 4240 papers

TitleStatusHype
Distilling Large Language Models for Efficient Clinical Information Extraction—0
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models—0
A New Method to Capturing Compositional Knowledge in Linguistic Space—0
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student PerformanceCode0
Self-Evolution Knowledge Distillation for LLM-based Machine Translation—0
Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image SegmentationCode0
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object DetectionCode0
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective—0
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process—0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
Hybrid Data-Free Knowledge DistillationCode0
On the Compression of Language Models for Code: An Empirical Study on CodeBERT—0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions—0
PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction—0
Modality-Inconsistent Continual Learning of Multimodal Large Language Models—0
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
Neural Collapse Inspired Knowledge Distillation—0
Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty DetectionCode0
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning—0
Knowledge Migration Framework for Smart Contract Vulnerability Detection—0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—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