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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 451–500 of 4240 papers

TitleStatusHype
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student PerformanceCode0
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object DetectionCode0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
A Survey on Inference Optimization Techniques for Mixture of Experts ModelsCode3
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image SegmentationCode2
Hybrid Data-Free Knowledge DistillationCode0
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process—0
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning PerspectiveCode0
On the Compression of Language Models for Code: An Empirical Study on CodeBERT—0
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction—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
Modality-Inconsistent Continual Learning of Multimodal Large Language Models—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts—0
Relation-Guided Adversarial Learning for Data-free Knowledge TransferCode1
BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video with Non-uniform MotionsCode2
Neural Collapse Inspired Knowledge Distillation—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Knowledge Migration Framework for Smart Contract Vulnerability Detection—0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes—0
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation—0
Leveraging Large Language Models for Active Merchant Non-player CharactersCode0
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning—0
Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty DetectionCode0
Can Students Beyond The Teacher? Distilling Knowledge from Teacher's Bias—0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
ScaleOT: Privacy-utility-scalable Offsite-tuning with Dynamic LayerReplace and Selective Rank Compression—0
Dynamic Contrastive Knowledge Distillation for Efficient Image RestorationCode1
Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices—0
Multimodal Industrial Anomaly Detection by Crossmodal Reverse DistillationCode0
All You Need in Knowledge Distillation Is a Tailored Coordinate System—0
SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training—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
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach—0
Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge DistillationCode2
DAKD: Data Augmentation and Knowledge Distillation using Diffusion Models for SAR Oil Spill Segmentation—0
Cloud Object Detector Adaptation by Integrating Different Source KnowledgeCode1
TT-MPD: Test Time Model Pruning and Distillation—0
Unlocking the Potential of Reverse Distillation for Anomaly DetectionCode1
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
Domain-Specific Translation with Open-Source Large Language Models: Resource-Oriented Analysis—0
Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation—0
Neighborhood Commonality-aware Evolution Network for Continuous Generalized Category DiscoveryCode0
CCS: Continuous Learning for Customized Incremental Wireless Sensing Services—0
BEExformer: A Fast Inferencing Transformer Architecture via Binarization with Multiple Early Exits—0
One-shot Federated Learning via Synthetic Distiller-Distillate CommunicationCode1
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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