HyperAI超神経
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HyperAI超神経

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

モデル名
Top 1 Accuracy
Paper TitleRepository
PAWS (ResNet-50)66.5%Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
Rotation-S4L: Self-Supervised Semi-Supervised Learning
SimCLRv2 (ResNet-152 x3, SK)74.9%Big Self-Supervised Models are Strong Semi-Supervised Learners
VICREG (Resnet-50)54.8%VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Exemplar (joint training)-S4L: Self-Supervised Semi-Supervised Learning
SEER (RegNet10B)62.4%Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
SimCLRv2 distilled (ResNet-50 x2, SK)75.9%Big Self-Supervised Models are Strong Semi-Supervised Learners
Semi-ViT (ViT-Base)71%Semi-supervised Vision Transformers at Scale
Barlow Twins (ResNet-50)55%Barlow Twins: Self-Supervised Learning via Redundancy Reduction
DebiasPL (ResNet-50)71.3%Debiased Learning from Naturally Imbalanced Pseudo-Labels
SimMatchV2 (ResNet-50)71.9%SimMatchV2: Semi-Supervised Learning with Graph Consistency
PAWS (ResNet-50 2x)69.6%Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
SimCLR (ResNet-50 4×)63.0%A Simple Framework for Contrastive Learning of Visual Representations
BYOL (ResNet-50)53.2%Bootstrap your own latent: A new approach to self-supervised Learning
CPC-Representation Learning with Contrastive Predictive Coding
SimCLRv2 distilled (ResNet-50)73.9%Big Self-Supervised Models are Strong Semi-Supervised Learners
SimCLR (ResNet-50 2×)58.5%A Simple Framework for Contrastive Learning of Visual Representations
CoMatch + EPASS (ResNet-50)67.4%Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
Exemplar-S4L: Self-Supervised Semi-Supervised Learning
CoMatch (w. MoCo v2)67.1%CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
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