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 3101–3150 of 4240 papers

TitleStatusHype
Yield Evaluation of Citrus Fruits based on the YoloV5 compressed by Knowledge Distillation—0
YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models -—0
You Can Have Your Data and Balance It Too: Towards Balanced and Efficient Multilingual Models—0
You Do Not Need Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement—0
Zero shot framework for satellite image restoration—0
Zero-shot Slot Filling in the Age of LLMs for Dialogue Systems—0
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Learning Efficient Object Detection Models with Knowledge Distillation—0
Learning from a Lightweight Teacher for Efficient Knowledge Distillation—0
Learning From Biased Soft Labels—0
Learning from deep model via exploring local targets—0
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL—0
Learning from Matured Dumb Teacher for Fine Generalization—0
Learning Human-Human Interactions in Images from Weak Textual Supervision—0
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing—0
Learning Interpretation with Explainable Knowledge Distillation—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation—0
Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation—0
Learning Modality-agnostic Representation for Semantic Segmentation from Any Modalities—0
Learning Student-Friendly Teacher Networks for Knowledge Distillation—0
Learning Student Networks via Feature Embedding—0
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion—0
Learning the Wrong Lessons: Inserting Trojans During Knowledge Distillation—0
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification—0
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
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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