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Semi Supervised Image Classification On 1

Metrics

Top 1 Accuracy

Results

Performance results of various models on this benchmark

Paper Title
REACT (ViT-Large)81.6%Learning Customized Visual Models with Retrieval-Augmented Knowledge
Meta Co-Training80.7%Meta Co-Training: Two Views are Better than One
Semi-ViT (ViT-Huge)80%Semi-supervised Vision Transformers at Scale
Semi-ViT (ViT-Large)77.3%Semi-supervised Vision Transformers at Scale
SimCLRv2 self-distilled (ResNet-152 x3, SK)76.6%Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLRv2 distilled (ResNet-50 x2, SK)75.9%Big Self-Supervised Models are Strong Semi-Supervised Learners
MSN (ViT-B/4)75.7%Masked Siamese Networks for Label-Efficient Learning
SimCLRv2 (ResNet-152 x3, SK)74.9%Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLRv2 distilled (ResNet-50)73.9%Big Self-Supervised Models are Strong Semi-Supervised Learners
SimMatchV2 (ResNet-50)71.9%SimMatchV2: Semi-Supervised Learning with Graph Consistency
DebiasPL (ResNet-50)71.3%Debiased Learning from Naturally Imbalanced Pseudo-Labels
BYOL (ResNet-200 x2)71.2%Bootstrap your own latent: A new approach to self-supervised Learning
Semi-ViT (ViT-Base)71%Semi-supervised Vision Transformers at Scale
PAWS (ResNet-50 4x)69.9%Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
PAWS (ResNet-50 2x)69.6%Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
BYOL (ResNet-50 x4)69.1%Bootstrap your own latent: A new approach to self-supervised Learning
SimMatch + EPASS (ResNet-50)68.6%Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
CoMatch + EPASS (ResNet-50)67.4%Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
TWIST (ResNet-50 x2)67.2%Self-Supervised Learning by Estimating Twin Class Distributions
SimMatch (ResNet-50)67.2%SimMatch: Semi-supervised Learning with Similarity Matching
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Semi Supervised Image Classification On 1 | SOTA | HyperAI