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 3951–4000 of 4240 papers

TitleStatusHype
Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation—0
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach—0
Learning to Project for Cross-Task Knowledge Distillation—0
Learning to reconstruct signals with inexact sensing operator via knowledge distillation—0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Learning to Teach with Student Feedback—0
Learning to Teach with Student Feedback—0
Learning ULMFiT and Self-Distillation with Calibration for Medical Dialogue System—0
Learning Using Generated Privileged Information by Text-to-Image Diffusion Models—0
Teaching What You Should Teach: A Data-Based Distillation Method—0
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data—0
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition—0
Learn to Talk via Proactive Knowledge Transfer—0
Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data—0
Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models—0
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision—0
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity—0
Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction—0
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation—0
Let Video Teaches You More: Video-to-Image Knowledge Distillation using DEtection TRansformer for Medical Video Lesion Detection—0
Letz Translate: Low-Resource Machine Translation for Luxembourgish—0
Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation—0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification—0
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval—0
Leveraging Angular Distributions for Improved Knowledge Distillation—0
Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation—0
Leveraging Conditional Mutual Information to Improve Large Language Model Fine-Tuning For Classification—0
Leveraging Different Learning Styles for Improved Knowledge Distillation in Biomedical Imaging—0
Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification—0
FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMs—0
Leveraging Foundation Models To learn the shape of semi-fluid deformable objects—0
Leveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency—0
Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies—0
Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition—0
Li3DeTr: A LiDAR based 3D Detection Transformer—0
Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification—0
Lifelong GAN: Continual Learning for Conditional Image Generation—0
Lifelong Intent Detection via Multi-Strategy Rebalancing—0
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation—0
Lifelong Learning for Neural powered Mixed Integer Programming—0
Lifelong Learning via Progressive Distillation and Retrospection—0
Lifelong Object Detection—0
Lifelong Person Search—0
Lifelong Twin Generative Adversarial Networks—0
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation—0
LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation—0
Light distillation for Incremental Graph Convolution Collaborative Filtering—0
LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning—0
LightVessel: Exploring Lightweight Coronary Artery Vessel Segmentation via Similarity Knowledge Distillation—0
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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