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

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Showing 51–100 of 167 papers

TitleStatusHype
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation AnchoringCode1
Representation Learning with Contrastive Predictive CodingCode1
[Re] Reimplementation of FixMatch and Investigation on Noisy (Pseudo) Labels and Confirmation Errors of FixMatchCode1
Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained LearningCode1
Self-Supervised Learning by Estimating Twin Class DistributionsCode1
Self-Supervised Learning of Pretext-Invariant RepresentationsCode1
Probabilistic Contrastive Learning for Domain AdaptationCode1
SemiReward: A General Reward Model for Semi-supervised LearningCode1
Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation FrameworkCode1
Semi-supervised Image Classification with Grad-CAM ConsistencyCode1
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support SamplesCode1
Semi-Supervised Learning with Context-Conditional Generative Adversarial NetworksCode1
Semi-Supervised Learning with Ladder NetworksCode1
Semi-Supervised Learning with Multi-Head Co-TrainingCode1
DMT: Dynamic Mutual Training for Semi-Supervised LearningCode1
Semi-Supervised Single-View 3D Reconstruction via Prototype Shape PriorsCode1
Semi-Supervised Vision TransformersCode1
Semi-supervised Vision Transformers at ScaleCode1
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO ApproximationsCode1
Shrinking Class Space for Enhanced Certainty in Semi-Supervised LearningCode1
SimMatchV2: Semi-Supervised Learning with Graph ConsistencyCode1
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised ClassificationCode1
Subspace Capsule NetworkCode1
SVFormer: Semi-supervised Video Transformer for Action RecognitionCode1
Temporal Self-Ensembling Teacher for Semi-Supervised Object DetectionCode1
Towards Semi-supervised Learning with Non-random Missing LabelsCode1
Unsupervised Data Augmentation for Consistency TrainingCode1
Unsupervised Feature Learning by Cross-Level Instance-Group DiscriminationCode1
Unsupervised Feature Learning via Non-Parametric Instance DiscriminationCode1
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised LearningCode1
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised LearningCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Weakly Supervised Contrastive LearningCode1
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning—0
Comment on "Ensemble Projection for Semi-supervised Image Classification"—0
Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Learning—0
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification—0
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning—0
Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers—0
Contrastive Regularization for Semi-Supervised Learning—0
CURL: Co-trained Unsupervised Representation Learning for Image Classification—0
Dash: Semi-Supervised Learning with Dynamic Thresholding—0
Improving Face Recognition by Clustering Unlabeled Faces in the Wild—0
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification—0
Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model—0
Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation—0
Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning—0
Diffusion-Based Representation Learning—0
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All datasetsImageNet - 10% labeled dataImageNet - 1% labeled dataCIFAR-10, 4000 Labelscifar-100, 10000 LabelsCIFAR-10, 250 LabelsSVHN, 1000 labelsSVHN, 250 LabelsCIFAR-10, 40 LabelsCIFAR-100, 400 LabelsCIFAR-10, 1000 LabelsCIFAR-100, 2500 LabelsSTL-10, 1000 Labels

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
5Semi-SST (ViT-Huge)Top 1 Accuracy80.7—Unverified
6Meta Co-TrainingTop 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