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 2126–2150 of 4240 papers

TitleStatusHype
Multi-Channel Multi-Domain based Knowledge Distillation Algorithm for Sleep Staging with Single-Channel EEG—0
SeqNAS: Neural Architecture Search for Event Sequence ClassificationCode0
CTC Blank Triggered Dynamic Layer-Skipping for Efficient CTC-based Speech Recognition—0
Exploring Vacant Classes in Label-Skewed Federated LearningCode0
Distillation-based fabric anomaly detectionCode0
Bridging Modalities: Knowledge Distillation and Masked Training for Translating Multi-Modal Emotion Recognition to Uni-Modal, Speech-Only Emotion RecognitionCode0
Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection—0
Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning—0
Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware DetectorCode0
Distilling Local Texture Features for Colorectal Tissue Classification in Low Data RegimesCode0
Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification—0
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing—0
Building Vision-Language Models on Solid Foundations with Masked Distillation—0
Distilling CLIP with Dual Guidance for Learning Discriminative Human Body Shape Representation—0
Uncertainty-Guided Never-Ending Learning to Drive—0
IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation—0
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential RecommendationCode0
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPCCode0
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation—0
KD-DETR: Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling—0
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples—0
Scene-adaptive and Region-aware Multi-modal Prompt for Open Vocabulary Object Detection—0
Compressing Deep Image Super-resolution Models—0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
ClST: A Convolutional Transformer Framework for Automatic Modulation Recognition by Knowledge Distillation—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