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
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