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 51100 of 167 papers

TitleStatusHype
Debiased Learning from Naturally Imbalanced Pseudo-LabelsCode1
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
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency RegularizationCode1
Semi-Supervised Vision TransformersCode1
iBOT: Image BERT Pre-Training with Online TokenizerCode1
Probabilistic Contrastive Learning for Domain AdaptationCode1
DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples0
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo LabelingCode2
Self-Supervised Learning by Estimating Twin Class DistributionsCode1
Weakly Supervised Contrastive LearningCode1
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Dash: Semi-Supervised Learning with Dynamic Thresholding0
Semi-supervised Image Classification with Grad-CAM ConsistencyCode1
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from EchocardiogramsCode1
Semi-Supervised Learning with Multi-Head Co-TrainingCode1
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification0
DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised LearningCode1
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised ClassificationCode1
Diffusion-Based Representation Learning0
Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle librariesCode0
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised LearningCode1
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual RepresentationsCode0
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support SamplesCode1
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-trainingCode1
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised ClassificationCode1
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene SegmentationCode1
Barlow Twins: Self-Supervised Learning via Redundancy ReductionCode1
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
Learning Transferable Visual Models From Natural Language SupervisionCode2
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification0
Exponential Moving Average Normalization for Self-supervised and Semi-supervised LearningCode1
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning0
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised LearningCode1
One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask FeaturesCode0
EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANsCode1
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised LearningCode1
[Re] Reimplementation of FixMatch and Investigation on Noisy (Pseudo) Labels and Confirmation Errors of FixMatchCode1
Boosting the Performance of Semi-Supervised Learning with Unsupervised ClusteringCode0
Boosting Contrastive Self-Supervised Learning with False Negative CancellationCode1
KeepAugment: A Simple Information-Preserving Data Augmentation ApproachCode1
CoMatch: Semi-supervised Learning with Contrastive Graph RegularizationCode1
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO ApproximationsCode1
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few LabelsCode0
LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching0
Implicit Rank-Minimizing AutoencoderCode1
Online Semi-Supervised Learning in Contextual Bandits with Episodic RewardCode0
Unsupervised Feature Learning by Cross-Level Instance-Group DiscriminationCode1
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning0
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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