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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 251–300 of 4240 papers

TitleStatusHype
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge DistillationCode1
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language ModelCode1
Self-Supervised Quantization-Aware Knowledge DistillationCode1
Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide ImagesCode1
SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike StreamsCode1
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network StructureCode1
eDifFIQA: Towards Efficient Face Image Quality Assessment Based On Denoising Diffusion Probabilistic ModelsCode1
Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic HashingCode1
Frequency Attention for Knowledge DistillationCode1
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR FeaturesCode1
Self-Adapting Large Visual-Language Models to Edge Devices across Visual ModalitiesCode1
Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object DetectionCode1
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt TuningCode1
Improve Cross-Architecture Generalization on Dataset DistillationCode1
Knowledge Distillation Based on Transformed Teacher MatchingCode1
GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph CreationCode1
LaCo: Large Language Model Pruning via Layer CollapseCode1
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated LearningCode1
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation ModelsCode1
Good Teachers Explain: Explanation-Enhanced Knowledge DistillationCode1
BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge DistillationCode1
Large Language Model Distilling Medication Recommendation ModelCode1
LQER: Low-Rank Quantization Error Reconstruction for LLMsCode1
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side DistillationCode1
Rethinking Centered Kernel Alignment in Knowledge DistillationCode1
HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation DistillationCode1
TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in ConversationCode1
Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual InformationCode1
Graph Relation Distillation for Efficient Biomedical Instance SegmentationCode1
HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing AidsCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental LearningCode1
DIOD: Self-Distillation Meets Object DiscoveryCode1
CaKDP: Category-aware Knowledge Distillation and Pruning Framework for Lightweight 3D Object DetectionCode1
FerKD: Surgical Label Adaptation for Efficient DistillationCode1
Federated Learning with Extremely Noisy Clients via Negative DistillationCode1
Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference SpeedCode1
DistilVPR: Cross-Modal Knowledge Distillation for Visual Place RecognitionCode1
Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion ModelsCode1
Simple Image-level Classification Improves Open-vocabulary Object DetectionCode1
Generative Model-based Feature Knowledge Distillation for Action RecognitionCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object DetectorCode1
KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp SegmentationCode1
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL ApproachCode1
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic SegmentationCode1
Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image EnhancementCode1
Dual-Teacher De-biasing Distillation Framework for Multi-domain Fake News DetectionCode1
Initializing Models with Larger OnesCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
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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