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 1861–1870 of 4240 papers

Title	Date	Tasks	Status	Hype
Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks	Jun 7, 2023	Audio ClassificationAudio Tagging	CodeCode Available	1
Model-Based Reinforcement Learning with Multi-Task Offline Pretraining	Jun 6, 2023	Knowledge DistillationModel-based Reinforcement Learning	CodeCode Available	0
Orca: Progressive Learning from Complex Explanation Traces of GPT-4	Jun 5, 2023	Imitation LearningKnowledge Distillation	CodeCode Available	1
Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs	Jun 5, 2023	Entity AlignmentKnowledge Distillation	CodeCode Available	0
Zero shot framework for satellite image restoration	Jun 5, 2023	DisentanglementImage Restoration	—Unverified	0
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers	Jun 4, 2023	Backdoor Defense for Data-Free Distillation with Poisoned TeachersData-free Knowledge Distillation	CodeCode Available	1
I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval	Jun 4, 2023	Knowledge DistillationPassage Retrieval	CodeCode Available	1
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference	Jun 4, 2023	DecoderKnowledge Distillation	—Unverified	0
Evolving Knowledge Mining for Class Incremental Segmentation	Jun 3, 2023	Class-Incremental Semantic SegmentationKnowledge Distillation	CodeCode Available	0
Deep Classifier Mimicry without Data Access	Jun 3, 2023	Knowledge 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