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 671–680 of 4240 papers

Title	Date	Tasks	Status	Hype
A framework for benchmarking class-out-of-distribution detection and its application to ImageNet	Feb 23, 2023	BenchmarkingKnowledge Distillation	CodeCode Available	1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation	May 19, 2021	Image ClassificationKnowledge Distillation	CodeCode Available	1
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering	Apr 26, 2023	DecoderKnowledge Distillation	CodeCode Available	1
Gradient-based Intra-attention Pruning on Pre-trained Language Models	Dec 15, 2022	Knowledge Distillation	CodeCode Available	1
Complementary Relation Contrastive Distillation	Mar 29, 2021	Knowledge DistillationRelation	CodeCode Available	1
Graph-Free Knowledge Distillation for Graph Neural Networks	May 16, 2021	Knowledge DistillationTransfer Learning	CodeCode Available	1
A Discrepancy Aware Framework for Robust Anomaly Detection	Oct 11, 2023	Anomaly DetectionDecoder	CodeCode Available	1
Graph-less Collaborative Filtering	Mar 15, 2023	Collaborative FilteringContrastive Learning	CodeCode Available	1
Continual evaluation for lifelong learning: Identifying the stability gap	May 26, 2022	Continual LearningIncremental Learning	CodeCode Available	1
DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation	May 2, 2023	Knowledge DistillationSemantic Segmentation	CodeCode Available	1

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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