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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 4101–4110 of 4240 papers

Title	Date	Tasks	Status	Hype
FEED: Feature-level Ensemble Effect for knowledge Distillation	May 1, 2019	Knowledge DistillationTransfer Learning	—Unverified	0
Towards a better understanding of Vector Quantized Autoencoders	May 1, 2019	Knowledge DistillationMachine Translation	—Unverified	0
Semi-supervised Acoustic Event Detection based on tri-training	Apr 29, 2019	Event DetectionKnowledge Distillation	—Unverified	0
Segmenting the Future	Apr 24, 2019	Autonomous DrivingDecision Making	CodeCode Available	0
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks	Apr 23, 2019	Image GenerationKnowledge Distillation	CodeCode Available	0
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System	Apr 21, 2019	Knowledge DistillationModel Compression	—Unverified	0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding	Apr 20, 2019	Ensemble LearningKnowledge Distillation	—Unverified	0
Knowledge Distillation via Route Constrained Optimization	Apr 19, 2019	Face RecognitionKnowledge Distillation	CodeCode Available	1
Feature Fusion for Online Mutual Knowledge Distillation	Apr 19, 2019	Knowledge Distillation	CodeCode Available	0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation	Apr 17, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0

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All datasets ImageNet CIFAR-100 COCO (Common Objects in Context)COCO 2017 val PASCAL VOC KITTI

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ScaleKD (T:BEiT-L S:ViT-B/14)	Top-1 accuracy %	86.43	—	Unverified
2	ScaleKD (T:Swin-L S:ViT-B/16)	Top-1 accuracy %	85.53	—	Unverified
3	ScaleKD (T:Swin-L S:ViT-S/16)	Top-1 accuracy %	83.93	—	Unverified
4	ScaleKD (T:Swin-L S:Swin-T)	Top-1 accuracy %	83.8	—	Unverified
5	KD++(T: regnety-16GF S:ViT-B)	Top-1 accuracy %	83.6	—	Unverified
6	VkD (T:RegNety 160 S:DeiT-S)	Top-1 accuracy %	82.9	—	Unverified
7	SpectralKD (T:Swin-S S:Swin-T)	Top-1 accuracy %	82.7	—	Unverified
8	ScaleKD (T:Swin-L S:ResNet-50)	Top-1 accuracy %	82.55	—	Unverified
9	DiffKD (T:Swin-L S: Swin-T)	Top-1 accuracy %	82.5	—	Unverified
10	DIST (T: Swin-L S: Swin-T)	Top-1 accuracy %	82.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SRD (T:resnet-32x4, S:shufflenet-v2)	Top-1 Accuracy (%)	79.86	—	Unverified
2	shufflenet-v2(T:resnet-32x4, S:shufflenet-v2)	Top-1 Accuracy (%)	78.76	—	Unverified
3	MV-MR (T: CLIP/ViT-B-16 S: resnet50)	Top-1 Accuracy (%)	78.6	—	Unverified
4	resnet8x4 (T: resnet32x4 S: resnet8x4)	Top-1 Accuracy (%)	78.28	—	Unverified
5	resnet8x4 (T: resnet32x4 S: resnet8x4 [modified])	Top-1 Accuracy (%)	78.08	—	Unverified
6	ReviewKD++(T:resnet-32x4, S:shufflenet-v2)	Top-1 Accuracy (%)	77.93	—	Unverified
7	ReviewKD++(T:resnet-32x4, S:shufflenet-v1)	Top-1 Accuracy (%)	77.68	—	Unverified
8	resnet8x4 (T: resnet32x4 S: resnet8x4)	Top-1 Accuracy (%)	77.5	—	Unverified
9	resnet8x4 (T: resnet32x4 S: resnet8x4)	Top-1 Accuracy (%)	76.68	—	Unverified
10	resnet8x4 (T: resnet32x4 S: resnet8x4)	Top-1 Accuracy (%)	76.31	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSHFM (T: ResNet101 S: ResNet50)	mAP	77.16	—	Unverified
2	LSHFM (T: ResNet101 S: MobileNetV2)	mAP	73.73	—	Unverified
3	ADLIK-Faster (T: Faster R-CNN vit-base S: Faster R-CNN deit-small)	box AP	47.6	—	Unverified
4	ADLIK-Mask (T: Mask R-CNN vit-base S: Mask R-CNN deit-small)	mask AP	42.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(resnet50))	AP@0.5	61.8	—	Unverified
2	ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(resnet18))	AP@0.5	57.96	—	Unverified
3	ReviewKD++(T: faster rcnn(resnet101), S:faster rcnn(mobilenet-v2))	AP@0.5	55.18	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSHFM (T: ResNet101 S: ResNet50)	mAP	93.17	—	Unverified
2	LSHFM (T: ResNet101 S: MobileNetV2)	mAP	90.14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TIE-KD (T: Adabins S: MobileNetV2)	RMSE	2.43	—	Unverified