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 3151–3200 of 4240 papers

TitleStatusHype
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—0
Co-Teaching for Unsupervised Domain Adaptation and ExpansionCode0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
DST: Dynamic Substitute Training for Data-free Black-box Attack—0
A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity RecognitionCode0
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment AnalysisCode0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation—0
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction—0
Preventing Distillation-based Attacks on Neural Network IP—0
Knowledge distillation with error-correcting transfer learning for wind power prediction—0
Unified and Effective Ensemble Knowledge Distillation—0
Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification—0
A Closer Look at Rehearsal-Free Continual Learning—0
Conditional Autoregressors are Interpretable Classifiers—0
Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models—0
RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging—0
Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches—0
Knowledge Distillation: Bad Models Can Be Good Role Models—0
A Cross-Domain Approach for Continuous Impression Recognition from Dyadic Audio-Visual-Physio Signals—0
Class-Incremental Learning for Action Recognition in Videos—0
Multitask Emotion Recognition Model with Knowledge Distillation and Task Discriminator—0
Scale-Equivalent Distillation for Semi-Supervised Object Detection—0
Towards Expressive Speaking Style Modelling with Hierarchical Context Information for Mandarin Speech Synthesis—0
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal—0
On Neural Network Equivalence Checking using SMT Solvers—0
Channel Self-Supervision for Online Knowledge Distillation—0
Emulating Quantum Dynamics with Neural Networks via Knowledge DistillationCode0
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients—0
Delta Distillation for Efficient Video ProcessingCode0
SC2 Benchmark: Supervised Compression for Split Computing—0
Domain Adaptive Hand Keypoint and Pixel Localization in the Wild—0
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation—0
On the benefits of knowledge distillation for adversarial robustness—0
DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network—0
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation—0
Medical Image Segmentation on MRI Images with Missing Modalities: A Review—0
Deep Class Incremental Learning from Decentralized DataCode0
Look Backward and Forward: Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation—0
Improving Neural ODEs via Knowledge Distillation—0
Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGACode0
Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation—0
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting—0
Efficient Sub-structured Knowledge DistillationCode0
PyNET-QxQ: An Efficient PyNET Variant for QxQ Bayer Pattern Demosaicing in CMOS Image SensorsCode0
On Generalizing Beyond Domains in Cross-Domain Continual Learning—0
Multi-trial Neural Architecture Search with Lottery Tickets—0
Enhance Language Identification using Dual-mode Model with Knowledge DistillationCode0
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain AdaptationCode0
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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