SOTAVerified

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:

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

Papers

Showing 126–150 of 167 papers

TitleStatusHype
SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learningCode0
One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask FeaturesCode0
SESS: Self-Ensembling Semi-Supervised 3D Object DetectionCode0
Online Semi-Supervised Learning in Contextual Bandits with Episodic RewardCode0
ViTSGMM: A Robust Semi-Supervised Image Recognition Network Using Sparse LabelsCode0
SimMatch: Semi-supervised Learning with Similarity MatchingCode0
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?Code0
RDA: Reciprocal Distribution Alignment for Robust Semi-supervised LearningCode0
RealMix: Towards Realistic Semi-Supervised Deep Learning AlgorithmsCode0
RelationMatch: Matching In-batch Relationships for Semi-supervised LearningCode0
Repetitive Reprediction Deep Decipher for Semi-Supervised LearningCode0
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head OptimizationCode0
S4L: Self-Supervised Semi-Supervised LearningCode0
Stacked What-Where Auto-encodersCode0
Boosting the Performance of Semi-Supervised Learning with Unsupervised ClusteringCode0
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled DataCode0
CapsuleGAN: Generative Adversarial Capsule NetworkCode0
Data-Efficient Image Recognition with Contrastive Predictive CodingCode0
Structured Generative Adversarial NetworksCode0
Deep Reference Priors: What is the best way to pretrain a model?Code0
DoubleMatch: Improving Semi-Supervised Learning with Self-SupervisionCode0
Dual Student: Breaking the Limits of the Teacher in Semi-supervised LearningCode0
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble TransformationsCode0
Deep Sparse Representation-based ClassificationCode0
Global-Local Regularization Via Distributional RobustnessCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SimCLR (ResNet-50 4×)Top 5 Accuracy92.6—Unverified
2Rotation + VAT + Ent. Min.Top 5 Accuracy91.23—Unverified
3SimCLR (ResNet-50 2×)Top 5 Accuracy91.2—Unverified
4Mean Teacher (ResNeXt-152)Top 5 Accuracy90.89—Unverified
5OBoW (ResNet-50)Top 5 Accuracy90.7—Unverified
6R2-D2 (ResNet-18)Top 5 Accuracy90.48—Unverified
7FixMatchTop 5 Accuracy89.13—Unverified
8UDATop 5 Accuracy88.52—Unverified
9SimCLR (ResNet-50)Top 5 Accuracy87.8—Unverified
10DHO (ViT-Large)Top 1 Accuracy85.9—Unverified
#ModelMetricClaimedVerifiedStatus
1DHO (ViT-Large)Top 1 Accuracy84.6—Unverified
2OBoW (ResNet-50)Top 5 Accuracy82.9—Unverified
3DHO (ViT-Base)Top 1 Accuracy81.6—Unverified
4REACT (ViT-Large)Top 1 Accuracy81.6—Unverified
5Meta Co-TrainingTop 1 Accuracy80.7—Unverified
6Semi-SST (ViT-Huge)Top 1 Accuracy80.7—Unverified
7Super-SST (ViT-Huge)Top 1 Accuracy80.3—Unverified
8Semi-ViT (ViT-Huge)Top 1 Accuracy80—Unverified
9Semi-ViT (ViT-Large)Top 1 Accuracy77.3—Unverified
10Super-SST (ViT-Small distilled)Top 1 Accuracy76.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Γ-modelPercentage error20.4—Unverified
2GANPercentage error15.59—Unverified
3Bad GANPercentage error14.41—Unverified
4Triple-GAN-V2 (CNN-13, no aug)Percentage error12.41—Unverified
5Pi ModelPercentage error12.16—Unverified
6SESEMI SSL (ConvNet)Percentage error11.65—Unverified
7VATPercentage error11.36—Unverified
8GLOT-DRPercentage error10.6—Unverified
9VAT+EntMinPercentage error10.55—Unverified
10Triple-GAN-V2 (CNN-13)Percentage error10.01—Unverified
#ModelMetricClaimedVerifiedStatus
1Ⅱ-ModelPercentage error39.19—Unverified
2SESEMI SSL (ConvNet)Percentage error38.7—Unverified
3Temporal ensemblingPercentage error38.65—Unverified
4R2-D2 (CNN-13)Percentage error32.87—Unverified
5Dual Student (480)Percentage error32.77—Unverified
6UPS (CNN-13)Percentage error32—Unverified
7SHOT-VAEPercentage error25.3—Unverified
8LiDAMPercentage error23.22—Unverified
9EnAET (WRN-28-2-Large)Percentage error22.92—Unverified
10FixMatch (RA, WRN-28-8)Percentage error22.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Ⅱ-ModelPercentage error53.12—Unverified
2MixUpPercentage error47.43—Unverified
3MeanTeacherPercentage error47.32—Unverified
4VATPercentage error36.03—Unverified
5LiDAMPercentage error19.17—Unverified
6MixMatchPercentage error11.08—Unverified
7RealMixPercentage error9.79—Unverified
8EnAETPercentage error7.6—Unverified
9ReMixMatchPercentage error6.27—Unverified
10FixMatch+CRPercentage error5.04—Unverified