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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4051–4075 of 4240 papers

TitleStatusHype
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient ModelsCode0
On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identificationCode0
Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid InferenceCode0
Low-Cost Self-Ensembles Based on Multi-Branch Transformation and Grouped ConvolutionCode0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID DataCode0
A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity RecognitionCode0
SynthDistill: Face Recognition with Knowledge Distillation from Synthetic DataCode0
Synthetic data generation method for data-free knowledge distillation in regression neural networksCode0
FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID DataCode0
Online Adversarial Knowledge Distillation for Graph Neural NetworksCode0
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural NetworksCode0
Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast ApproachCode0
Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable AggregationCode0
SCJD: Sparse Correlation and Joint Distillation for Efficient 3D Human Pose EstimationCode0
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object DetectionCode0
Online Ensemble Model Compression using Knowledge DistillationCode0
Understanding the Effect of Model Compression on Social Bias in Large Language ModelsCode0
Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography ImagesCode0
Feature Normalized Knowledge Distillation for Image ClassificationCode0
An Embarrassingly Simple Approach for Knowledge DistillationCode0
Declarative Knowledge Distillation from Large Language Models for Visual Question Answering DatasetsCode0
Feature Fusion for Online Mutual Knowledge DistillationCode0
Online Knowledge Distillation with Diverse PeersCode0
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge DistillationCode0
Towards Mitigating Architecture Overfitting on Distilled DatasetsCode0
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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