Image Classification on cifar-100, 10000 Labels

Metric: Percentage error (lower is better)

LeaderboardDataset

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#	Model↕	Percentage error▲	Extra Data	Paper	Date↕	Code
1	CCSSL(FixMatch)	19.32	No	Class-Aware Contrastive Semi-Supervised Learning	2022-03-04	Code
2	SimMatch	20.58	No	SimMatch: Semi-supervised Learning with Similari...	2022-03-14	Code
3	FixMatch+CR	21.03	No	Contrastive Regularization for Semi-Supervised L...	2022-01-17	-
4	NP-Match	21.22	No	NP-Match: When Neural Processes meet Semi-Superv...	2022-07-03	Code
5	SMPL (WRN-28-8)	21.68	No	Self Meta Pseudo Labels: Meta Pseudo Labels With...	2022-12-27	-
6	FreeMatch	21.68	No	FreeMatch: Self-adaptive Thresholding for Semi-s...	2022-05-15	Code
7	SimPLE (WRN-28-8)	21.89	No	SimPLE: Similar Pseudo Label Exploitation for Se...	2021-03-30	Code
8	FixMatch (RA, WRN-28-8)	22.6	No	FixMatch: Simplifying Semi-Supervised Learning w...	2020-01-21	Code
9	EnAET (WRN-28-2-Large)	22.92	No	EnAET: A Self-Trained framework for Semi-Supervi...	2019-11-21	Code
10	LiDAM	23.22	No	LiDAM: Semi-Supervised Learning with Localized D...	2020-10-13	-
11	SHOT-VAE	25.3	No	SHOT-VAE: Semi-supervised Deep Generative Models...	2020-11-21	Code
12	UPS (CNN-13)	32	No	In Defense of Pseudo-Labeling: An Uncertainty-Aw...	2021-01-15	Code
13	Dual Student (480)	32.77	No	Dual Student: Breaking the Limits of the Teacher...	2019-09-03	Code
14	R2-D2 (CNN-13)	32.87	No	Repetitive Reprediction Deep Decipher for Semi-S...	2019-08-09	Code
15	Temporal ensembling	38.65	No	Temporal Ensembling for Semi-Supervised Learning	2016-10-07	Code
16	SESEMI SSL (ConvNet)	38.7	No	Exploring Self-Supervised Regularization for Sup...	2019-06-25	Code
17	Ⅱ-Model	39.19	No	Regularization With Stochastic Transformations a...	2016-06-14	-

#1CCSSL(FixMatch)SOTA
19.32
Percentage error· 2022-03-04
Class-Aware Contrastive Semi-Supervised Learning Code
#2SimMatch
20.58
Percentage error· 2022-03-14
SimMatch: Semi-supervised Learning with Similarity Matching Code
#3FixMatch+CRSOTA
21.03
Percentage error· 2022-01-17
Contrastive Regularization for Semi-Supervised Learning
#4NP-Match
21.22
Percentage error· 2022-07-03
NP-Match: When Neural Processes meet Semi-Supervised Learning Code
#5SMPL (WRN-28-8)
21.68
Percentage error· 2022-12-27
Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher
#6FreeMatch
21.68
Percentage error· 2022-05-15
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning Code
#7SimPLE (WRN-28-8)SOTA
21.89
Percentage error· 2021-03-30
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification Code
#8FixMatch (RA, WRN-28-8)SOTA
22.6
Percentage error· 2020-01-21
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Code
#9EnAET (WRN-28-2-Large)SOTA
22.92
Percentage error· 2019-11-21
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations Code
#10LiDAM
23.22
Percentage error· 2020-10-13
LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching
#11SHOT-VAE
25.3
Percentage error· 2020-11-21
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations Code
#12UPS (CNN-13)
32
Percentage error· 2021-01-15
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Code
#13Dual Student (480)SOTA
32.77
Percentage error· 2019-09-03
Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning Code
#14R2-D2 (CNN-13)SOTA
32.87
Percentage error· 2019-08-09
Repetitive Reprediction Deep Decipher for Semi-Supervised Learning Code
#15Temporal ensemblingSOTA
38.65
Percentage error· 2016-10-07
Temporal Ensembling for Semi-Supervised Learning Code
#16SESEMI SSL (ConvNet)
38.7
Percentage error· 2019-06-25
Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning Code
#17Ⅱ-ModelSOTA
39.19
Percentage error· 2016-06-14
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning