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 1701–1725 of 4240 papers

TitleStatusHype
LLAVADI: What Matters For Multimodal Large Language Models Distillation—0
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network—0
Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D TransformersCode0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction—0
Leveraging Foundation Models via Knowledge Distillation in Multi-Object Tracking: Distilling DINOv2 Features to FairMOTCode0
NC-NCD: Novel Class Discovery for Node ClassificationCode0
Separating Novel Features for Logical Anomaly Detection: A Straightforward yet Effective Approach—0
How to Train the Teacher Model for Effective Knowledge DistillationCode0
Peak-Controlled Logits Poisoning Attack in Federated Distillation—0
CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in TomosynthesisCode0
DDK: Distilling Domain Knowledge for Efficient Large Language Models—0
Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical videoCode0
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
SeqMIA: Sequential-Metric Based Membership Inference AttackCode0
Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification—0
Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge DistillationCode0
Continual Panoptic Perception: Towards Multi-modal Incremental Interpretation of Remote Sensing ImagesCode0
DFMSD: Dual Feature Masking Stage-wise Knowledge Distillation for Object Detection—0
Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation—0
Make a Strong Teacher with Label Assistance: A Novel Knowledge Distillation Approach for Semantic SegmentationCode0
Continual Distillation Learning: Knowledge Distillation in Prompt-based Continual Learning—0
QuIIL at T3 challenge: Towards Automation in Life-Saving Intervention Procedures from First-Person ViewCode0
Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model—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