Semi-Supervised Image Classification

Semi-supervised image classification leverages unlabelled data as well as labelled data to increase classification performance.

You may want to read some blog posts to get an overview before reading the papers and checking the leaderboards:

An overview of proxy-label approaches for semi-supervised learning - Sebastian Ruder
Semi-Supervised Learning in Computer Vision - Amit Chaudhary

( Image credit: Self-Supervised Semi-Supervised Learning )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 101–125 of 167 papers

Title	Date	Tasks	Status	Hype
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning	Jul 16, 2020	ClusteringData Augmentation	CodeCode Available	1
Improving Face Recognition by Clustering Unlabeled Faces in the Wild	Jul 14, 2020	ClusteringFace Clustering	—Unverified	0
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection	Jul 13, 2020	image-classificationImage Classification	CodeCode Available	1
Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Learning	Jul 8, 2020	image-classificationImage Classification	—Unverified	0
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments	Jun 17, 2020	Contrastive LearningData Augmentation	CodeCode Available	2
Big Self-Supervised Models are Strong Semi-Supervised Learners	Jun 17, 2020	Self-Supervised Image ClassificationSemi-Supervised Image Classification	CodeCode Available	2
Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance	Jun 16, 2020	Semi-Supervised Image Classification	CodeCode Available	1
Bootstrap your own latent: A new approach to self-supervised Learning	Jun 13, 2020	Image ClassificationLinear evaluation	CodeCode Available	1
SCAN: Learning to Classify Images without Labels	May 25, 2020	ClassificationClustering	CodeCode Available	2
A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency	May 19, 2020	ClassificationGeneral Classification	CodeCode Available	1
Prototypical Contrastive Learning of Unsupervised Representations	May 11, 2020	ClusteringContrastive Learning	CodeCode Available	1
DMT: Dynamic Mutual Training for Semi-Supervised Learning	Apr 18, 2020	image-classificationImage Classification	CodeCode Available	1
Milking CowMask for Semi-Supervised Image Classification	Mar 26, 2020	ClassificationGeneral Classification	CodeCode Available	0
Meta Pseudo Labels	Mar 23, 2020	Image ClassificationMeta-Learning	CodeCode Available	1
A Simple Framework for Contrastive Learning of Visual Representations	Feb 13, 2020	Contrastive LearningImage Classification	CodeCode Available	2
Subspace Capsule Network	Feb 7, 2020	General ClassificationGenerative Adversarial Network	CodeCode Available	1
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence	Jan 21, 2020	Image ClassificationPseudo Label	CodeCode Available	2
batchboost: regularization for stabilizing training with resistance to underfitting & overfitting	Jan 21, 2020	Image ClassificationSemi-Supervised Image Classification	CodeCode Available	1
Semi-Supervised Learning with Normalizing Flows	Dec 30, 2019	image-classificationImage Classification	CodeCode Available	0
SESS: Self-Ensembling Semi-Supervised 3D Object Detection	Dec 26, 2019	3D Object Detectionimage-classification	CodeCode Available	0
Triple Generative Adversarial Networks	Dec 20, 2019	ClassificationConditional Image Generation	CodeCode Available	0
RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms	Dec 18, 2019	Deep LearningSemi-Supervised Image Classification	CodeCode Available	0
Self-Supervised Learning of Pretext-Invariant Representations	Dec 4, 2019	Contrastive Learningobject-detection	CodeCode Available	1
Flow Contrastive Estimation of Energy-Based Models	Dec 2, 2019	Image GenerationSemi-Supervised Image Classification	CodeCode Available	1
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring	Nov 21, 2019	Image ClassificationSemi-Supervised Image Classification	CodeCode Available	1

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All datasets ImageNet - 10% labeled data ImageNet - 1% labeled data CIFAR-10, 4000 Labels cifar-100, 10000 Labels CIFAR-10, 250 Labels SVHN, 1000 labels SVHN, 250 Labels CIFAR-10, 40 Labels CIFAR-100, 400 Labels CIFAR-10, 1000 Labels CIFAR-100, 2500 Labels STL-10, 1000 Labels

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SimCLR (ResNet-50 4×)	Top 5 Accuracy	92.6	—	Unverified
2	Rotation + VAT + Ent. Min.	Top 5 Accuracy	91.23	—	Unverified
3	SimCLR (ResNet-50 2×)	Top 5 Accuracy	91.2	—	Unverified
4	Mean Teacher (ResNeXt-152)	Top 5 Accuracy	90.89	—	Unverified
5	OBoW (ResNet-50)	Top 5 Accuracy	90.7	—	Unverified
6	R2-D2 (ResNet-18)	Top 5 Accuracy	90.48	—	Unverified
7	FixMatch	Top 5 Accuracy	89.13	—	Unverified
8	UDA	Top 5 Accuracy	88.52	—	Unverified
9	SimCLR (ResNet-50)	Top 5 Accuracy	87.8	—	Unverified
10	DHO (ViT-Large)	Top 1 Accuracy	85.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DHO (ViT-Large)	Top 1 Accuracy	84.6	—	Unverified
2	OBoW (ResNet-50)	Top 5 Accuracy	82.9	—	Unverified
3	DHO (ViT-Base)	Top 1 Accuracy	81.6	—	Unverified
4	REACT (ViT-Large)	Top 1 Accuracy	81.6	—	Unverified
5	Semi-SST (ViT-Huge)	Top 1 Accuracy	80.7	—	Unverified
6	Meta Co-Training	Top 1 Accuracy	80.7	—	Unverified
7	Super-SST (ViT-Huge)	Top 1 Accuracy	80.3	—	Unverified
8	Semi-ViT (ViT-Huge)	Top 1 Accuracy	80	—	Unverified
9	Semi-ViT (ViT-Large)	Top 1 Accuracy	77.3	—	Unverified
10	Super-SST (ViT-Small distilled)	Top 1 Accuracy	76.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Γ-model	Percentage error	20.4	—	Unverified
2	GAN	Percentage error	15.59	—	Unverified
3	Bad GAN	Percentage error	14.41	—	Unverified
4	Triple-GAN-V2 (CNN-13, no aug)	Percentage error	12.41	—	Unverified
5	Pi Model	Percentage error	12.16	—	Unverified
6	SESEMI SSL (ConvNet)	Percentage error	11.65	—	Unverified
7	VAT	Percentage error	11.36	—	Unverified
8	GLOT-DR	Percentage error	10.6	—	Unverified
9	VAT+EntMin	Percentage error	10.55	—	Unverified
10	Triple-GAN-V2 (CNN-13)	Percentage error	10.01	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ⅱ-Model	Percentage error	39.19	—	Unverified
2	SESEMI SSL (ConvNet)	Percentage error	38.7	—	Unverified
3	Temporal ensembling	Percentage error	38.65	—	Unverified
4	R2-D2 (CNN-13)	Percentage error	32.87	—	Unverified
5	Dual Student (480)	Percentage error	32.77	—	Unverified
6	UPS (CNN-13)	Percentage error	32	—	Unverified
7	SHOT-VAE	Percentage error	25.3	—	Unverified
8	LiDAM	Percentage error	23.22	—	Unverified
9	EnAET (WRN-28-2-Large)	Percentage error	22.92	—	Unverified
10	FixMatch (RA, WRN-28-8)	Percentage error	22.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ⅱ-Model	Percentage error	53.12	—	Unverified
2	MixUp	Percentage error	47.43	—	Unverified
3	MeanTeacher	Percentage error	47.32	—	Unverified
4	VAT	Percentage error	36.03	—	Unverified
5	LiDAM	Percentage error	19.17	—	Unverified
6	MixMatch	Percentage error	11.08	—	Unverified
7	RealMix	Percentage error	9.79	—	Unverified
8	EnAET	Percentage error	7.6	—	Unverified
9	ReMixMatch	Percentage error	6.27	—	Unverified
10	FixMatch+CR	Percentage error	5.04	—	Unverified