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 1951–2000 of 4240 papers

TitleStatusHype
On the Surprising Efficacy of Distillation as an Alternative to Pre-Training Small ModelsCode0
Improve Knowledge Distillation via Label Revision and Data Selection—0
Foundation Models for Structural Health MonitoringCode0
Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
Federated Distillation: A Survey—0
Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation—0
Task Integration Distillation for Object Detectors—0
Class-Incremental Few-Shot Event Detection—0
LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation—0
SUGAR: Pre-training 3D Visual Representations for Robotics—0
A Comprehensive Review of Knowledge Distillation in Computer Vision—0
Weak-to-Strong 3D Object Detection with X-Ray DistillationCode0
DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object DetectionCode0
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts—0
GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation—0
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation—0
I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation—0
Enhancing Metaphor Detection through Soft Labels and Target Word Prediction—0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
Oh! We Freeze: Improving Quantized Knowledge Distillation via Signal Propagation Analysis for Large Language Models—0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—0
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation—0
Configurable Holography: Towards Display and Scene Adaptation—0
Learning to Project for Cross-Task Knowledge Distillation—0
Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated LearningCode0
Facilitating Pornographic Text Detection for Open-Domain Dialogue Systems via Knowledge Distillation of Large Language ModelsCode0
TransformMix: Learning Transformation and Mixing Strategies from Data—0
HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View DistillationCode0
Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces—0
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models—0
KnFu: Effective Knowledge Fusion—0
Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image ClassificationCode0
FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning—0
LookALike: Human Mimicry based collaborative decision making—0
Group-Mix SAM: Lightweight Solution for Industrial Assembly Line Applications—0
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models—0
Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials—0
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization—0
MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine TranslationCode0
An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task LearningCode0
CoroNetGAN: Controlled Pruning of GANs via Hypernetworks—0
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving—0
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer—0
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models—0
Low-Energy On-Device Personalization for MCUsCode0
Distilling the Knowledge in Data Pruning—0
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context LearningCode0
One Category One Prompt: Dataset Distillation using Diffusion Models—0
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge DistillationCode0
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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