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 151–200 of 4240 papers

TitleStatusHype
Adaptive Multi-Teacher Knowledge Distillation with Meta-LearningCode1
Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal HashingCode1
Cross-category Video Highlight Detection via Set-based LearningCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Contrastive Distillation on Intermediate Representations for Language Model CompressionCode1
Contrastive Representation DistillationCode1
Cross-modality Data Augmentation for End-to-End Sign Language TranslationCode1
Data-Free Class-Incremental Hand Gesture RecognitionCode1
Continual Collaborative Distillation for Recommender SystemCode1
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network StructureCode1
Continual evaluation for lifelong learning: Identifying the stability gapCode1
Content-Variant Reference Image Quality Assessment via Knowledge DistillationCode1
Content-Aware GAN CompressionCode1
Context-Aware Image Inpainting with Learned Semantic PriorsCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Consistent Representation Learning for Continual Relation ExtractionCode1
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy AnnotationsCode1
ConStyle v2: A Strong Prompter for All-in-One Image RestorationCode1
CoNMix for Source-free Single and Multi-target Domain AdaptationCode1
Conformer and Blind Noisy Students for Improved Image Quality AssessmentCode1
ConNER: Consistency Training for Cross-lingual Named Entity RecognitionCode1
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse DataCode1
Computation-Efficient Knowledge Distillation via Uncertainty-Aware MixupCode1
ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian SplattingCode1
Comprehensive Knowledge Distillation with Causal InterventionCode1
Complementary Relation Contrastive DistillationCode1
Compressing Deep Graph Neural Networks via Adversarial Knowledge DistillationCode1
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side DistillationCode1
A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG DataCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
Model LEGO: Creating Models Like Disassembling and Assembling Building BlocksCode1
CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic SegmentationCode1
Coaching a Teachable StudentCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
Confidence-Aware Multi-Teacher Knowledge DistillationCode1
Contrastive Deep SupervisionCode1
Data-Free Knowledge Distillation for Heterogeneous Federated LearningCode1
CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as TeachersCode1
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
AdaptGuard: Defending Against Universal Attacks for Model AdaptationCode1
CLRKDNet: Speeding up Lane Detection with Knowledge DistillationCode1
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual DistillationCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
AIM 2024 Challenge on UHD Blind Photo Quality AssessmentCode1
Class-Incremental Learning by Knowledge Distillation with Adaptive Feature ConsolidationCode1
Collaborative Distillation for Ultra-Resolution Universal Style TransferCode1
COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using TransformersCode1
Class-incremental Novel Class DiscoveryCode1
CLIP-KD: An Empirical Study of CLIP Model DistillationCode1
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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