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 1351–1400 of 4240 papers

TitleStatusHype
Distilling Large Language Models for Efficient Clinical Information Extraction—0
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models—0
A New Method to Capturing Compositional Knowledge in Linguistic Space—0
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student PerformanceCode0
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object DetectionCode0
Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image SegmentationCode0
Self-Evolution Knowledge Distillation for LLM-based Machine Translation—0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
Hybrid Data-Free Knowledge DistillationCode0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
On the Compression of Language Models for Code: An Empirical Study on CodeBERT—0
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective—0
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process—0
Modality-Inconsistent Continual Learning of Multimodal Large Language Models—0
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions—0
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts—0
Neural Collapse Inspired Knowledge Distillation—0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes—0
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation—0
Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty DetectionCode0
Knowledge Migration Framework for Smart Contract Vulnerability Detection—0
Leveraging Large Language Models for Active Merchant Non-player CharactersCode0
ScaleOT: Privacy-utility-scalable Offsite-tuning with Dynamic LayerReplace and Selective Rank Compression—0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
Can Students Beyond The Teacher? Distilling Knowledge from Teacher's Bias—0
Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices—0
All You Need in Knowledge Distillation Is a Tailored Coordinate System—0
DASK: Distribution Rehearsing via Adaptive Style Kernel Learning for Exemplar-Free Lifelong Person Re-IdentificationCode0
A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks—0
Multimodal Industrial Anomaly Detection by Crossmodal Reverse DistillationCode0
SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training—0
DAKD: Data Augmentation and Knowledge Distillation using Diffusion Models for SAR Oil Spill Segmentation—0
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach—0
TT-MPD: Test Time Model Pruning and Distillation—0
FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question AnsweringCode0
U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening—0
Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation—0
Domain-Specific Translation with Open-Source Large Language Models: Resource-Oriented Analysis—0
Neighborhood Commonality-aware Evolution Network for Continuous Generalized Category DiscoveryCode0
BEExformer: A Fast Inferencing Transformer Architecture via Binarization with Multiple Early Exits—0
CCS: Continuous Learning for Customized Incremental Wireless Sensing Services—0
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation—0
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated LearningCode0
Expanding Deep Learning-based Sensing Systems with Multi-Source Knowledge Transfer—0
Enhancing CLIP Conceptual Embedding through Knowledge Distillation—0
Show:102550
← PrevPage 28 of 85Next →
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