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 1–10 of 167 papers

TitleStatusHype
ViTSGMM: A Robust Semi-Supervised Image Recognition Network Using Sparse LabelsCode0
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning—0
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head OptimizationCode0
Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision—0
Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification—0
SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual RepresentationsCode0
Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification—0
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image ClassificationCode1
Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FixMatch+CRPercentage error27.58—Unverified
2LiDAMPercentage error26.5—Unverified
3FreeMatchPercentage error26.47—Unverified
4NP-MatchPercentage error26.03—Unverified
5ShrinkMatchPercentage error25.17—Unverified
6SimMatchPercentage error25.07—Unverified
7CCSSL(FixMatch)Percentage error24.3—Unverified
8SemiOccamPercentage error22.19—Unverified