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 451–475 of 4240 papers

TitleStatusHype
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student PerformanceCode0
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object DetectionCode0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Hybrid Data-Free Knowledge DistillationCode0
A Survey on Inference Optimization Techniques for Mixture of Experts ModelsCode3
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image SegmentationCode2
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process—0
On the Compression of Language Models for Code: An Empirical Study on CodeBERT—0
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective—0
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction—0
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions—0
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts—0
Modality-Inconsistent Continual Learning of Multimodal Large Language Models—0
BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video with Non-uniform MotionsCode2
Neural Collapse Inspired Knowledge Distillation—0
Relation-Guided Adversarial Learning for Data-free Knowledge TransferCode1
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Leveraging Large Language Models for Active Merchant Non-player CharactersCode0
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation—0
Knowledge Migration Framework for Smart Contract Vulnerability Detection—0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes—0
Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty DetectionCode0
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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