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 3301–3325 of 4240 papers

TitleStatusHype
Model Compression for Resource-Constrained Mobile Robots—0
Model Compression Methods for YOLOv5: A Review—0
Model compression using knowledge distillation with integrated gradients—0
Model Compression Using Optimal Transport—0
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System—0
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System—0
Model Distillation for Faithful Explanations of Medical Code Predictions—0
Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification—0
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks—0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation—0
Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples—0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation—0
Model Stitching by Functional Latent Alignment—0
Modifying Final Splits of Classification Tree for Fine-tuning Subpopulation Target in Policy Making—0
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference—0
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation—0
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router—0
MoKD: Multi-Task Optimization for Knowledge Distillation—0
MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation—0
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation—0
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching—0
More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data—0
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation—0
MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders—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