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 1681–1690 of 4240 papers

Title	Date	Tasks	Status	Hype
A deep Natural Language Inference predictor without language-specific training data	Sep 6, 2023	Aspect-Based Sentiment AnalysisKnowledge Distillation	—Unverified	0
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation	Sep 5, 2023	Image CompressionKnowledge Distillation	—Unverified	0
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models	Sep 5, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Probabilistic Self-supervised Learning via Scoring Rules Minimization	Sep 5, 2023	Knowledge DistillationOut-of-Distribution Detection	—Unverified	0
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking	Sep 5, 2023	BenchmarkingKnowledge Distillation	—Unverified	0
Prior Knowledge Guided Network for Video Anomaly Detection	Sep 4, 2023	Anomaly DetectionKnowledge Distillation	—Unverified	0
On the Query Strategies for Efficient Online Active Distillation	Sep 4, 2023	Active LearningContinual Learning	—Unverified	0
COMEDIAN: Self-Supervised Learning and Knowledge Distillation for Action Spotting using Transformers	Sep 3, 2023	Action DetectionAction Spotting	CodeCode Available	1
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts	Aug 31, 2023	Contrastive LearningGraph Classification	—Unverified	0
MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis	Aug 31, 2023	Contrastive LearningKnowledge Distillation	CodeCode Available	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