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 3851–3900 of 4240 papers

TitleStatusHype
Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom—0
Knowledge Distillation of LLM for Automatic Scoring of Science Education Assessments—0
Knowledge Distillation of Transformer-based Language Models Revisited—0
Knowledge Distillation on Graphs: A Survey—0
Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction—0
Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models—0
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks—0
Knowledge Distillation Under Ideal Joint Classifier Assumption—0
Knowledge Distillation Using Frontier Open-source LLMs: Generalizability and the Role of Synthetic Data—0
Knowledge distillation using unlabeled mismatched images—0
Knowledge distillation via adaptive instance normalization—0
Knowledge Distillation via Instance-level Sequence Learning—0
Knowledge Distillation via Query Selection for Detection Transformer—0
Knowledge distillation via softmax regression representation learning—0
Knowledge Distillation via Token-level Relationship Graph—0
Knowledge Distillation via Weighted Ensemble of Teaching Assistants—0
Knowledge Distillation vs. Pretraining from Scratch under a Fixed (Computation) Budget—0
Knowledge distillation with a class-aware loss for endoscopic disease detection—0
Knowledge Distillation with Adapted Weight—0
Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays—0
Knowledge Distillation with BERT for Image Tag-Based Privacy Prediction—0
Knowledge distillation with error-correcting transfer learning for wind power prediction—0
Knowledge Distillation with Feature Maps for Image Classification—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
Knowledge Distillation with Noisy Labels for Natural Language Understanding—0
Representative Teacher Keys for Knowledge Distillation Model Compression Based on Attention Mechanism for Image Classification—0
Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping—0
Knowledge Distillation with Training Wheels—0
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection—0
EA-KD: Entropy-based Adaptive Knowledge Distillation—0
Knowledge Consistency between Neural Networks and Beyond—0
Knowledge Migration Framework for Smart Contract Vulnerability Detection—0
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation—0
Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity—0
Knowledge Squeezed Adversarial Network Compression—0
Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation—0
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning—0
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning—0
Know your tools well: Better and faster QA with synthetic examples—0
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis—0
KoGNER: A Novel Framework for Knowledge Graph Distillation on Biomedical Named Entity Recognition—0
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation—0
KroneckerBERT: Significant Compression of Pre-trained Language Models Through Kronecker Decomposition and Knowledge Distillation—0
Kronecker Decomposition for GPT Compression—0
KTAN: Knowledge Transfer Adversarial Network—0
Label Assignment Distillation for Object Detection—0
Label Augmentation via Time-based Knowledge Distillation for Financial Anomaly Detection—0
Label-Context-Dependent Internal Language Model Estimation for CTC—0
Label Denoising with Large Ensembles of Heterogeneous Neural Networks—0
Label driven Knowledge Distillation for Federated Learning with non-IID Data—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