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 3051–3100 of 4240 papers

TitleStatusHype
One General Teacher for Multi-Data Multi-Task: A New Knowledge Distillation Framework for Discourse Relation Analysis—0
Self-Distilled Pruning of Neural Networks—0
Making Small Language Models Better Few-Shot Learners—0
Feature Structure Distillation for BERT Transferring—0
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching—0
Learning to Teach with Student Feedback—0
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm—0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—0
NVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21—0
Synthetic Unknown Class Learning for Learning Unknowns—0
Robust and Accurate Object Detection via Self-Knowledge DistillationCode0
Facial Landmark Points Detection Using Knowledge Distillation-Based Neural NetworksCode0
Learning Interpretation with Explainable Knowledge Distillation—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
On Representation Knowledge Distillation for Graph Neural NetworksCode1
A Survey on Green Deep Learning—0
Class Token and Knowledge Distillation for Multi-head Self-Attention Speaker Verification Systems—0
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models—0
Visualizing the Emergence of Intermediate Visual Patterns in DNNs—0
DVFL: A Vertical Federated Learning Method for Dynamic Data—0
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family—0
A methodology for training homomorphicencryption friendly neural networks—0
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval—0
LTD: Low Temperature Distillation for Robust Adversarial Training—0
Knowledge Cross-Distillation for Membership Privacy—0
The LMU Munich System for the WMT 2021 Large-Scale Multilingual Machine Translation Shared Task—0
The NiuTrans System for the WMT 2021 Efficiency Task—0
NVIDIA NeMo’s Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21—0
Papago’s Submission for the WMT21 Quality Estimation Shared Task—0
The Mininglamp Machine Translation System for WMT21—0
HW-TSC’s Participation in the WMT 2021 News Translation Shared Task—0
HW-TSC’s Participation in the WMT 2021 Large-Scale Multilingual Translation Task—0
TenTrans Large-Scale Multilingual Machine Translation System for WMT21—0
Efficient Machine Translation with Model Pruning and Quantization—0
AUTOSUMM: Automatic Model Creation for Text Summarization—0
Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification—0
Universal-KD: Attention-based Output-Grounded Intermediate Layer Knowledge Distillation—0
Exploring Non-Autoregressive Text Style TransferCode0
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style TransferCode0
deepQuest-py: Large and Distilled Models for Quality EstimationCode0
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition—0
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation NetworksCode0
GAML-BERT: Improving BERT Early Exiting by Gradient Aligned Mutual Learning—0
Improving Stance Detection with Multi-Dataset Learning and Knowledge DistillationCode0
Mutual-Learning Improves End-to-End Speech Translation—0
RW-KD: Sample-wise Loss Terms Re-Weighting for Knowledge Distillation—0
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation—0
Distilling Knowledge for Empathy DetectionCode0
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?—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