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

Showing 701725 of 4240 papers

TitleStatusHype
A Discrepancy Aware Framework for Robust Anomaly DetectionCode1
Join the High Accuracy Club on ImageNet with A Binary Neural Network TicketCode1
Extending global-local view alignment for self-supervised learning with remote sensing imageryCode1
Attention Distillation: self-supervised vision transformer students need more guidanceCode1
AdaDistill: Adaptive Knowledge Distillation for Deep Face RecognitionCode1
Cross-Layer Distillation with Semantic CalibrationCode1
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network StructureCode1
Confidence-Aware Multi-Teacher Knowledge DistillationCode1
Dice Semimetric Losses: Optimizing the Dice Score with Soft LabelsCode1
Knowledge Diffusion for DistillationCode1
Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better TransferabilityCode1
Knowledge Distillation based Degradation Estimation for Blind Super-ResolutionCode1
Conformer and Blind Noisy Students for Improved Image Quality AssessmentCode1
Knowledge Distillation for BERT Unsupervised Domain AdaptationCode1
CoNMix for Source-free Single and Multi-target Domain AdaptationCode1
ConNER: Consistency Training for Cross-lingual Named Entity RecognitionCode1
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy AnnotationsCode1
Consistent Representation Learning for Continual Relation ExtractionCode1
CrossKD: Cross-Head Knowledge Distillation for Object DetectionCode1
ConStyle v2: A Strong Prompter for All-in-One Image RestorationCode1
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
Content-Aware GAN CompressionCode1
Camera clustering for scalable stream-based active distillationCode1
Content-Variant Reference Image Quality Assessment via Knowledge DistillationCode1
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ScaleKD (T:BEiT-L S:ViT-B/14)Top-1 accuracy %86.43Unverified
2ScaleKD (T:Swin-L S:ViT-B/16)Top-1 accuracy %85.53Unverified
3ScaleKD (T:Swin-L S:ViT-S/16)Top-1 accuracy %83.93Unverified
4ScaleKD (T:Swin-L S:Swin-T)Top-1 accuracy %83.8Unverified
5KD++(T: regnety-16GF S:ViT-B)Top-1 accuracy %83.6Unverified
6VkD (T:RegNety 160 S:DeiT-S)Top-1 accuracy %82.9Unverified
7SpectralKD (T:Swin-S S:Swin-T)Top-1 accuracy %82.7Unverified
8ScaleKD (T:Swin-L S:ResNet-50)Top-1 accuracy %82.55Unverified
9DiffKD (T:Swin-L S: Swin-T)Top-1 accuracy %82.5Unverified
10DIST (T: Swin-L S: Swin-T)Top-1 accuracy %82.3Unverified
#ModelMetricClaimedVerifiedStatus
1SRD (T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)79.86Unverified
2shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)78.76Unverified
3MV-MR (T: CLIP/ViT-B-16 S: resnet50)Top-1 Accuracy (%)78.6Unverified
4resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)78.28Unverified
5resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])Top-1 Accuracy (%)78.08Unverified
6ReviewKD++(T:resnet-32x4, S:shufflenet-v2)Top-1 Accuracy (%)77.93Unverified
7ReviewKD++(T:resnet-32x4, S:shufflenet-v1)Top-1 Accuracy (%)77.68Unverified
8resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)77.5Unverified
9resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.68Unverified
10resnet8x4 (T: resnet32x4 S: resnet8x4)Top-1 Accuracy (%)76.31Unverified
#ModelMetricClaimedVerifiedStatus
1LSHFM (T: ResNet101 S: ResNet50)mAP93.17Unverified
2LSHFM (T: ResNet101 S: MobileNetV2)mAP90.14Unverified
#ModelMetricClaimedVerifiedStatus
1TIE-KD (T: Adabins S: MobileNetV2)RMSE2.43Unverified