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 1401–1425 of 4240 papers

TitleStatusHype
Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification—0
Distillation of Diffusion Features for Semantic Correspondence—0
Mutli-View 3D Reconstruction using Knowledge DistillationCode0
QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval—0
Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps—0
Continuous Concepts Removal in Text-to-image Diffusion Models—0
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation—0
Reverse Thinking Makes LLMs Stronger Reasoners—0
Zero-shot Slot Filling in the Age of LLMs for Dialogue Systems—0
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs—0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling—0
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG—0
Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery—0
Active Data Curation Effectively Distills Large-Scale Multimodal Models—0
Words Matter: Leveraging Individual Text Embeddings for Code Generation in CLIP Test-Time AdaptationCode0
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics—0
Ensemble Learning via Knowledge Transfer for CTR PredictionCode0
Dynamic Self-Distillation via Previous Mini-batches for Fine-tuning Small Language Models—0
When Babies Teach Babies: Can student knowledge sharing outperform Teacher-Guided Distillation on small datasets?Code0
Leveraging Foundation Models To learn the shape of semi-fluid deformable objects—0
TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction—0
Efficient Ternary Weight Embedding Model: Bridging Scalability and PerformanceCode0
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning—0
Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven 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