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 4051–4100 of 4240 papers

TitleStatusHype
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement—0
Improved Techniques for Training Adaptive Deep NetworksCode1
Language Graph Distillation for Low-Resource Machine Translation—0
Knowledge distillation for semi-supervised domain adaptation—0
Adaptive Regularization of Labels—0
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence EmbeddingCode0
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations—0
Knowledge Consistency between Neural Networks and Beyond—0
Self-Knowledge Distillation in Natural Language Processing—0
Learning Lightweight Lane Detection CNNs by Self Attention DistillationCode0
Baidu Neural Machine Translation Systems for WMT19—0
The NiuTrans Machine Translation Systems for WMT19—0
GTCOM Neural Machine Translation Systems for WMT19—0
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation—0
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation—0
Distilled Siamese Networks for Visual Tracking—0
Lifelong GAN: Continual Learning for Conditional Image Generation—0
Real-Time Correlation Tracking via Joint Model Compression and TransferCode0
Similarity-Preserving Knowledge Distillation—0
Highlight Every Step: Knowledge Distillation via Collaborative TeachingCode0
Light Multi-segment Activation for Model CompressionCode0
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition—0
BAM! Born-Again Multi-Task Networks for Natural Language UnderstandingCode0
Graph-based Knowledge Distillation by Multi-head Attention NetworkCode0
Compression of Acoustic Event Detection Models With Quantized Distillation—0
Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning—0
Essence Knowledge Distillation for Speech Recognition—0
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog SystemsCode0
GAN-Knowledge Distillation for one-stage Object Detection—0
Membership Privacy for Machine Learning Models Through Knowledge Transfer—0
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks—0
Scalable Syntax-Aware Language Models Using Knowledge Distillation—0
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation—0
Incremental Classifier Learning Based on PEDCC-Loss and Cosine Distance—0
Distilling Object Detectors with Fine-grained Feature ImitationCode0
When Does Label Smoothing Help?Code1
Private Deep Learning with Teacher Ensembles—0
An Adaptive Random Path Selection Approach for Incremental LearningCode0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
On Knowledge distillation from complex networks for response prediction—0
Online Distilling from Checkpoints for Neural Machine Translation—0
Knowledge Distillation via Instance Relationship GraphCode0
Structured Knowledge Distillation for Semantic SegmentationCode0
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient ModelsCode0
Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation—0
Adversarially Robust DistillationCode1
Network Pruning via Transformable Architecture SearchCode0
Zero-Shot Knowledge Distillation in Deep NetworksCode0
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self DistillationCode0
Creating Lightweight Object Detectors with Model Compression for Deployment on Edge Devices—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