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 126–150 of 4240 papers

TitleStatusHype
Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition—0
Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal—0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization—0
Biomed-DPT: Dual Modality Prompt Tuning for Biomedical Vision-Language ModelsCode0
ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via α-β-DivergenceCode1
Theoretical Guarantees for LT-TTD: A Unified Transformer-based Architecture for Two-Level Ranking Systems—0
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges—0
SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation—0
Knowledge Distillation for Speech Denoising by Latent Representation Alignment with Cosine Distance—0
Image Recognition with Online Lightweight Vision Transformer: A SurveyCode0
Artificial Behavior Intelligence: Technology, Challenges, and Future Directions—0
End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning—0
AKD : Adversarial Knowledge Distillation For Large Language Models Alignment on Coding tasks—0
Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques—0
FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain GeneralizationCode0
Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge DistillationCode2
Segment Any RGB-Thermal Model with Language-aided Distillation—0
High-Fidelity Pseudo-label Generation by Large Language Models for Training Robust Radiology Report Classifiers—0
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach—0
Llama-Nemotron: Efficient Reasoning Models—0
Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading—0
Enhancing New-item Fairness in Dynamic Recommender SystemsCode0
CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation—0
How to Backdoor the Knowledge Distillation—0
Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks—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