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プラットフォーム
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SOTA
半教師あり画像分類
Semi Supervised Image Classification On 1
Semi Supervised Image Classification On 1
評価指標
Top 1 Accuracy
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Top 1 Accuracy
Paper Title
REACT (ViT-Large)
81.6%
Learning Customized Visual Models with Retrieval-Augmented Knowledge
Meta Co-Training
80.7%
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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