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 1101–1125 of 4240 papers

TitleStatusHype
Swapped Logit Distillation via Bi-level Teacher AlignmentCode0
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs—0
Unified Attacks to Large Language Model Watermarks: Spoofing and Scrubbing in Unauthorized Knowledge Distillation—0
Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?—0
Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification—0
Turbo2K: Towards Ultra-Efficient and High-Quality 2K Video Synthesis—0
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
Scaling Laws for Data-Efficient Visual Transfer Learning—0
Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption—0
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned DataCode0
Optimizing Multi-Gateway LoRaWAN via Cloud-Edge Collaboration and Knowledge Distillation—0
Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?—0
Knowledge Distillation for Multimodal Egocentric Action Recognition Robust to Missing Modalities—0
Proxy-Anchor and EVT-Driven Continual Learning Method for Generalized Category DiscoveryCode0
Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images—0
ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based RefinementCode0
Towards Unconstrained 2D Pose Estimation of the Human Spine—0
WK-Pnet: FM-Based Positioning via Wavelet Packet Decomposition and Knowledge Distillation—0
Distilling Knowledge from Heterogeneous Architectures for Semantic Segmentation—0
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer—0
Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework—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