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 101150 of 167 papers

TitleStatusHype
Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher0
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework0
OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation LearningCode0
RDA: Reciprocal Distribution Alignment for Robust Semi-supervised LearningCode0
DoubleMatch: Improving Semi-Supervised Learning with Self-SupervisionCode0
SimMatch: Semi-supervised Learning with Similarity MatchingCode0
Global-Local Regularization Via Distributional RobustnessCode0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without SupervisionCode0
Deep Reference Priors: What is the best way to pretrain a model?Code0
Contrastive Regularization for Semi-Supervised Learning0
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?Code0
An analysis of over-sampling labeled data in semi-supervised learning with FixMatchCode0
Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning0
DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples0
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Dash: Semi-Supervised Learning with Dynamic Thresholding0
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification0
Diffusion-Based Representation Learning0
Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle librariesCode0
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual RepresentationsCode0
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification0
Self-supervised Pretraining of Visual Features in the WildCode0
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification0
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning0
One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask FeaturesCode0
Boosting the Performance of Semi-Supervised Learning with Unsupervised ClusteringCode0
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few LabelsCode0
LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching0
Online Semi-Supervised Learning in Contextual Bandits with Episodic RewardCode0
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning0
Improving Face Recognition by Clustering Unlabeled Faces in the Wild0
Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Learning0
Milking CowMask for Semi-Supervised Image ClassificationCode0
Semi-Supervised Learning with Normalizing FlowsCode0
SESS: Self-Ensembling Semi-Supervised 3D Object DetectionCode0
Triple Generative Adversarial NetworksCode0
RealMix: Towards Realistic Semi-Supervised Deep Learning AlgorithmsCode0
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble TransformationsCode0
Adversarial Transformations for Semi-Supervised Learning0
Dual Student: Breaking the Limits of the Teacher in Semi-supervised LearningCode0
Repetitive Reprediction Deep Decipher for Semi-Supervised LearningCode0
Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation0
Exploring Self-Supervised Regularization for Supervised and Semi-Supervised LearningCode0
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy0
Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification0
Data-Efficient Image Recognition with Contrastive Predictive CodingCode0
Semi-Supervised Learning by Augmented Distribution AlignmentCode0
S4L: Self-Supervised Semi-Supervised LearningCode0
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text0
Deep Sparse Representation-based ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SimCLR (ResNet-50 4×)Top 5 Accuracy92.6Unverified
2Rotation + VAT + Ent. Min.Top 5 Accuracy91.23Unverified
3SimCLR (ResNet-50 2×)Top 5 Accuracy91.2Unverified
4Mean Teacher (ResNeXt-152)Top 5 Accuracy90.89Unverified
5OBoW (ResNet-50)Top 5 Accuracy90.7Unverified
6R2-D2 (ResNet-18)Top 5 Accuracy90.48Unverified
7FixMatchTop 5 Accuracy89.13Unverified
8UDATop 5 Accuracy88.52Unverified
9SimCLR (ResNet-50)Top 5 Accuracy87.8Unverified
10DHO (ViT-Large)Top 1 Accuracy85.9Unverified
#ModelMetricClaimedVerifiedStatus
1DHO (ViT-Large)Top 1 Accuracy84.6Unverified
2OBoW (ResNet-50)Top 5 Accuracy82.9Unverified
3DHO (ViT-Base)Top 1 Accuracy81.6Unverified
4REACT (ViT-Large)Top 1 Accuracy81.6Unverified
5Semi-SST (ViT-Huge)Top 1 Accuracy80.7Unverified
6Meta Co-TrainingTop 1 Accuracy80.7Unverified
7Super-SST (ViT-Huge)Top 1 Accuracy80.3Unverified
8Semi-ViT (ViT-Huge)Top 1 Accuracy80Unverified
9Semi-ViT (ViT-Large)Top 1 Accuracy77.3Unverified
10Super-SST (ViT-Small distilled)Top 1 Accuracy76.9Unverified
#ModelMetricClaimedVerifiedStatus
1Γ-modelPercentage error20.4Unverified
2GANPercentage error15.59Unverified
3Bad GANPercentage error14.41Unverified
4Triple-GAN-V2 (CNN-13, no aug)Percentage error12.41Unverified
5Pi ModelPercentage error12.16Unverified
6SESEMI SSL (ConvNet)Percentage error11.65Unverified
7VATPercentage error11.36Unverified
8GLOT-DRPercentage error10.6Unverified
9VAT+EntMinPercentage error10.55Unverified
10Triple-GAN-V2 (CNN-13)Percentage error10.01Unverified
#ModelMetricClaimedVerifiedStatus
1Ⅱ-ModelPercentage error39.19Unverified
2SESEMI SSL (ConvNet)Percentage error38.7Unverified
3Temporal ensemblingPercentage error38.65Unverified
4R2-D2 (CNN-13)Percentage error32.87Unverified
5Dual Student (480)Percentage error32.77Unverified
6UPS (CNN-13)Percentage error32Unverified
7SHOT-VAEPercentage error25.3Unverified
8LiDAMPercentage error23.22Unverified
9EnAET (WRN-28-2-Large)Percentage error22.92Unverified
10FixMatch (RA, WRN-28-8)Percentage error22.6Unverified
#ModelMetricClaimedVerifiedStatus
1Ⅱ-ModelPercentage error53.12Unverified
2MixUpPercentage error47.43Unverified
3MeanTeacherPercentage error47.32Unverified
4VATPercentage error36.03Unverified
5LiDAMPercentage error19.17Unverified
6MixMatchPercentage error11.08Unverified
7RealMixPercentage error9.79Unverified
8EnAETPercentage error7.6Unverified
9ReMixMatchPercentage error6.27Unverified
10FixMatch+CRPercentage error5.04Unverified