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Semi Supervised Image Classification On 2

Métriques

Top 1 Accuracy
Top 5 Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
Semi-ViT (ViT-Huge)84.3%96.6%Semi-supervised Vision Transformers at Scale
SimCLRv2 self-distilled (ResNet-152 x3, SK)80.9%95.5%Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLRv2 (ResNet-152 x3, SK)80.1%95.0%Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLRv2 distilled (ResNet-50 x2, SK)80.2%95.0%Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLRv2 distilled (ResNet-50)77.5%93.4%Big Self-Supervised Models are Strong Semi-Supervised Learners
TWIST (ResNet-50 x2)75.3%92.8%Self-Supervised Learning by Estimating Twin Class Distributions
SimMatch + EPASS (ResNet-50)75.3%92.6Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
SimCLR (ResNet-50 4×)-92.6%A Simple Framework for Contrastive Learning of Visual Representations
SequenceMatch (ResNet-50)75.2%91.9SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
SimCLRv2 (ResNet-50 x2)73.9%91.9%Big Self-Supervised Models are Strong Semi-Supervised Learners
CoMatch + EPASS (ResNet-50)74.1%91.5Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
CoMatch (w. MoCo v2)73.7%91.4%CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
Meta Pseudo Labels (ResNet-50)73.89%91.38%Meta Pseudo Labels
CowMix (ResNet-152)73.94%91.24%Milking CowMask for Semi-Supervised Image Classification
S4L-MOAM (ResNet-50 4×)73.21%91.23%S4L: Self-Supervised Semi-Supervised Learning
Rotation + VAT + Ent. Min.-91.23%S4L: Self-Supervised Semi-Supervised Learning
WCL (ResNet-50)72.0%91.2%Weakly Supervised Contrastive Learning
SimCLR (ResNet-50 2×)-91.2%A Simple Framework for Contrastive Learning of Visual Representations
CPC v2 (ResNet-161)73.1%91.2%Data-Efficient Image Recognition with Contrastive Predictive Coding
RELICv2 (ResNet-50)72.4%91.2%Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
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