Semi Supervised Image Classification On Cifar

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Percentage error

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Percentage error
Paper TitleRepository
GAN15.59Improved Techniques for Training GANs-
SimMatch3.96SimMatch: Semi-supervised Learning with Similarity Matching-
Self Meta Pseudo Labels4.09Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher-
FixMatch+DM4.13±0.11--
LiDAM7.48LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching-
Meta Pseudo Labels (WRN-28-2)3.89± 0.07Meta Pseudo Labels-
LaplaceNet (CNN-13)4.99±0.08LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification-
ReMixMatch5.14ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring-
EnAET4.18EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations-
DoubleMatch4.65±0.17DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision-
UDA5.27Unsupervised Data Augmentation for Consistency Training-
VAT11.36Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning-
Triple-GAN-V2 (CNN-13)10.01Triple Generative Adversarial Networks-
GLOT-DR10.6Global-Local Regularization Via Distributional Robustness-
FlexMatch4.19±0.01FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling-
UPS (Shake-Shake)4.86In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning-
SWSA5There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average-
Diff-SySC3.26±0.06Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification-
Dual Student (600)8.89Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning-
Dash (RA, ours)4.08±0.06Dash: Semi-Supervised Learning with Dynamic Thresholding-
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Semi Supervised Image Classification On Cifar | SOTA | HyperAI초신경