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 3451–3500 of 4240 papers

TitleStatusHype
Feature Kernel Distillation—0
Feature-map-level Online Adversarial Knowledge Distillation—0
Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification—0
Feature Structure Distillation for BERT Transferring—0
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
FedDKD: Federated Learning with Decentralized Knowledge Distillation—0
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks—0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction—0
FedEFM: Federated Endovascular Foundation Model with Unseen Data—0
Federated Action Recognition on Heterogeneous Embedded Devices—0
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process—0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization—0
Federated Distillation: A Survey—0
Federated Ensemble Model-based Reinforcement Learning in Edge Computing—0
Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions—0
Federated Graph Learning with Graphless Clients—0
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients—0
Federated Learning for Data and Model Heterogeneity in Medical Imaging—0
Federated Learning on Non-iid Data via Local and Global Distillation—0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—0
Federated One-Shot Learning with Data Privacy and Objective-Hiding—0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer—0
Federated Unlearning with Knowledge Distillation—0
FedKD: Communication Efficient Federated Learning via Knowledge Distillation—0
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning—0
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher—0
FedRAD: Federated Robust Adaptive Distillation—0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning—0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation—0
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation—0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning—0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction—0
FEED: Feature-level Ensemble Effect for knowledge Distillation—0
FEED: Feature-level Ensemble for Knowledge Distillation—0
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving—0
Few-shot Face Image Translation via GAN Prior Distillation—0
Few-shot learning of neural networks from scratch by pseudo example optimization—0
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations—0
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework—0
A methodology for training homomorphicencryption friendly neural networks—0
FiGKD: Fine-Grained Knowledge Distillation via High-Frequency Detail Transfer—0
Fine-Grained Distillation for Long Document Retrieval—0
Fine-grained Image Retrieval via Dual-Vision Adaptation—0
Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization—0
Fine-tuning a Multiple Instance Learning Feature Extractor with Masked Context Modelling and Knowledge Distillation—0
Boosting Pruned Networks with Linear Over-parameterization—0
Fixing the Teacher-Student Knowledge Discrepancy in Distillation—0
FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition—0
FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning—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