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Semi Supervised Image Classification On Cifar 2

Metrics

Percentage error

Results

Performance results of various models on this benchmark

Paper Title
Ⅱ-Model39.19Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
SESEMI SSL (ConvNet)38.7Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning
Temporal ensembling38.65Temporal Ensembling for Semi-Supervised Learning
R2-D2 (CNN-13)32.87Repetitive Reprediction Deep Decipher for Semi-Supervised Learning
Dual Student (480)32.77Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning
UPS (CNN-13)32In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
EnAET (WRN-28-2)26.93±0.21EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
SHOT-VAE25.3SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
SemCo (μ=7)24.45±0.12All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
LiDAM23.22LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching
FixMatch (CTA, WRN-28-8)23.18±0.11FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
CowMix (WRN-28-96x2d)23.07±0.30Milking CowMask for Semi-Supervised Image Classification
EnAET (WRN-28-2-Large)22.92EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
FixMatch (RA, WRN-28-8)22.6FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
DP-SSL22.24±0.31DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples
LaplaceNet (WRN-28-8)22.11± 0.23LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
Dash (RA, WRN-28-8)21.97±0.14Dash: Semi-Supervised Learning with Dynamic Thresholding
FlexMatch21.90±0.15FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
SimPLE (WRN-28-8)21.89SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification
SMPL (WRN-28-8)21.68Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher
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