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
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
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification0
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy0
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework0
Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification0
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning0
OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning0
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning0
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification0
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning0
Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers0
Color-S^4L: Self-supervised Semi-supervised Learning with Image Colorization0
Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher0
Comment on "Ensemble Projection for Semi-supervised Image Classification"0
Improving Face Recognition by Clustering Unlabeled Faces in the Wild0
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification0
Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Learning0
Contrastive Regularization for Semi-Supervised Learning0
Show:102550
← PrevPage 2 of 4Next →

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