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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
Knowledge Distillation for Mobile Edge Computation Offloading—0
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression—0
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation—0
Enhancing Review Comprehension with Domain-Specific Commonsense—0
Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge—0
Spatio-Temporal Graph for Video Captioning with Knowledge Distillation—0
SS-IL: Separated Softmax for Incremental Learning—0
Analysis of Knowledge Transfer in Kernel Regime—0
Squeezed Deep 6DoF Object Detection Using Knowledge DistillationCode0
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning—0
A Survey of Methods for Low-Power Deep Learning and Computer Vision—0
Teacher-Student chain for efficient semi-supervised histology image classification—0
Knowledge distillation via adaptive instance normalization—0
Pacemaker: Intermediate Teacher Knowledge Distillation For On-The-Fly Convolutional Neural Network—0
Explaining Knowledge Distillation by Quantifying the Knowledge—0
Distilling portable Generative Adversarial Networks for Image Translation—0
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation—0
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation—0
Residual Knowledge Distillation—0
Balancing Cost and Benefit with Tied-Multi Transformers—0
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding—0
Self-Distillation Amplifies Regularization in Hilbert Space—0
Content Based Singing Voice Extraction From a Musical MixtureCode0
Meta-Learning across Meta-Tasks for Few-Shot Learning—0
Regularized Evolutionary Population-Based Training—0
Understanding and Improving Knowledge Distillation—0
Unlabeled Data Deployment for Classification of Diabetic Retinopathy Images Using Knowledge Transfer—0
Feature-map-level Online Adversarial Knowledge Distillation—0
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement LearningCode0
Search for Better Students to Learn Distilled Knowledge—0
MSE-Optimal Neural Network Initialization via Layer FusionCode0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings—0
Data Techniques For Online End-to-end Speech Recognition—0
Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning—0
A "Network Pruning Network" Approach to Deep Model Compression—0
Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification—0
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation—0
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture SearchCode0
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion—0
Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation—0
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier—0
Data-Free Adversarial DistillationCode0
The State of Knowledge Distillation for ClassificationCode0
Joint Architecture and Knowledge Distillation in CNN for Chinese Text Recognition—0
Iterative Dual Domain Adaptation for Neural Machine Translation—0
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation—0
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation—0
QUEST: Quantized embedding space for transferring knowledgeCode0
Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation—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