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Semi Supervised Image Classification On Cifar 9

Métriques

Percentage error

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Percentage error
Paper TitleRepository
ReMixMatch27.43±0.31ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring-
CCSSL(FixMatch)24.3Class-Aware Contrastive Semi-Supervised Learning-
DoubleMatch27.07± 0.26DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision-
FixMatch+DM25.88±0.23--
LiDAM26.50LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching-
ShrinkMatch25.17Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning-
NP-Match26.03NP-Match: When Neural Processes meet Semi-Supervised Learning-
FixMatch (CTA, WRN-28-8)28.64±0.24FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence-
SimMatch25.07SimMatch: Semi-supervised Learning with Similarity Matching-
FreeMatch26.47FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning-
Dash (RA, WRN-28-8)27.18±0.21Dash: Semi-Supervised Learning with Dynamic Thresholding-
FixMatch+CR27.58Contrastive Regularization for Semi-Supervised Learning-
FlexMatch26.49±0.20FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling-
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Semi Supervised Image Classification On Cifar 9 | SOTA | HyperAI