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 2676–2700 of 4240 papers

TitleStatusHype
Ensemble knowledge distillation of self-supervised speech models—0
A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image—0
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision TransformersCode0
Personalized Decentralized Federated Learning with Knowledge Distillation—0
Exploring Social Media for Early Detection of Depression in COVID-19 PatientsCode0
Practical Knowledge Distillation: Using DNNs to Beat DNNs—0
Debiased Distillation by Transplanting the Last Layer—0
Distilling Calibrated Student from an Uncalibrated Teacher—0
KS-DETR: Knowledge Sharing in Attention Learning for Detection TransformerCode0
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled RegularizationCode0
Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection—0
The Role of Masking for Efficient Supervised Knowledge Distillation of Vision Transformers—0
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers—0
RobustDistiller: Compressing Universal Speech Representations for Enhanced Environment Robustness—0
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning—0
Explicit and Implicit Knowledge Distillation via Unlabeled Data—0
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving—0
Learning From Biased Soft Labels—0
Cross Modal Distillation for Flood Extent Mapping—0
Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK—0
LEALLA: Learning Lightweight Language-agnostic Sentence Embeddings with Knowledge Distillation—0
New Insights on Relieving Task-Recency Bias for Online Class Incremental LearningCode0
ST-MFNet Mini: Knowledge Distillation-Driven Frame InterpolationCode0
Offline-to-Online Knowledge Distillation for Video Instance Segmentation—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—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