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 2351–2400 of 4240 papers

TitleStatusHype
Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition—0
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons—0
Training Domain Draft Models for Speculative Decoding: Best Practices and Insights—0
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer—0
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks—0
Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification—0
TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction—0
Transferable Deployment of Semantic Edge Inference Systems via Unsupervised Domain Adaption—0
Transfer Learning with Pre-trained Conditional Generative Models—0
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing—0
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing—0
Transferring Learning Trajectories of Neural Networks—0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation—0
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation—0
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI—0
TransformMix: Learning Transformation and Mixing Strategies from Data—0
Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation—0
Tree Knowledge Distillation for Compressing Transformer-Based Language Models—0
Tree-Like Decision Distillation—0
TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology—0
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction—0
TRILLsson: Distilled Universal Paralinguistic Speech Representations—0
Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning—0
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching—0
Triplet Knowledge Distillation—0
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation—0
TrustAL: Trustworthy Active Learning using Knowledge Distillation—0
TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines—0
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models—0
TT-MPD: Test Time Model Pruning and Distillation—0
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models—0
Turbo2K: Towards Ultra-Efficient and High-Quality 2K Video Synthesis—0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization—0
Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection—0
Two-Pass End-to-End ASR Model Compression—0
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation—0
Two-Step Knowledge Distillation for Tiny Speech Enhancement—0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
Multi-trial Neural Architecture Search with Lottery Tickets—0
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation—0
UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation—0
U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening—0
Ultrafast Video Attention Prediction with Coupled Knowledge Distillation—0
Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces—0
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation—0
Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading—0
Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification—0
Uncertainty-Guided Never-Ending Learning to Drive—0
Understanding Adversarial Attacks on Autoencoders—0
Show:102550
← PrevPage 48 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