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 1001–1050 of 4240 papers

TitleStatusHype
Boosting Residual Networks with Group KnowledgeCode0
Discourse Structures Guided Fine-grained Propaganda IdentificationCode0
Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based GamesCode0
Less-supervised learning with knowledge distillation for sperm morphology analysisCode0
MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation DetectionCode0
Detect, Distill and Update: Detect, Distill and Update: Learned DB Systems Facing Out of Distribution DataCode0
Analyzing the Confidentiality of Undistillable Teachers in Knowledge DistillationCode0
Adaptive Search-and-Training for Robust and Efficient Network PruningCode0
Learning Lightweight Lane Detection CNNs by Self Attention DistillationCode0
Distill2Vec: Dynamic Graph Representation Learning with Knowledge DistillationCode0
An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question AnsweringCode0
Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D TransformersCode0
Learning Efficient Detector with Semi-supervised Adaptive DistillationCode0
Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal DistillationCode0
Learning Deep and Compact Models for Gesture RecognitionCode0
Delta Distillation for Efficient Video ProcessingCode0
Learning from Noisy Crowd Labels with LogicsCode0
Learning to Maximize Mutual Information for Chain-of-Thought DistillationCode0
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code GenerationCode0
Leaning Compact and Representative Features for Cross-Modality Person Re-IdentificationCode0
Language-Universal Adapter Learning with Knowledge Distillation for End-to-End Multilingual Speech RecognitionCode0
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
Blind Knowledge Distillation for Robust Image ClassificationCode0
BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge DistillationCode0
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series ForecastingCode0
deepQuest-py: Large and Distilled Models for Quality EstimationCode0
Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation RecognitionCode0
KS-DETR: Knowledge Sharing in Attention Learning for Detection TransformerCode0
Language Model Knowledge Distillation for Efficient Question Answering in SpanishCode0
Knowledge Distillation with Deep SupervisionCode0
Knowledge Extraction with No Observable DataCode0
Knowledge Grafting of Large Language ModelsCode0
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from ServerCode0
Deep geometric knowledge distillation with graphsCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual DataCode0
Deep Clustering with Diffused Sampling and Hardness-aware Self-distillationCode0
Knowledge Distillation with Adversarial Samples Supporting Decision BoundaryCode0
Deep Class Incremental Learning from Decentralized DataCode0
Deep Classifier Mimicry without Data AccessCode0
DED: Diagnostic Evidence Distillation for acne severity grading on face imagesCode0
Biomed-DPT: Dual Modality Prompt Tuning for Biomedical Vision-Language ModelsCode0
Knowledge Transfer Graph for Deep Collaborative LearningCode0
Knowledge Distillation Performs Partial Variance ReductionCode0
Knowledge Distillation of Russian Language Models with Reduction of VocabularyCode0
Knowledge distillation to effectively attain both region-of-interest and global semantics from an image where multiple objects appearCode0
Decoupled Knowledge with Ensemble Learning for Online DistillationCode0
Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object DetectionCode0
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student PerformanceCode0
Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion modelsCode0
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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