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ResearchFormerly WizWand

Image Classification on CIFAR-10 (250 labels)

2.42Error RateSemi-SST
0.267214.798629.3343.8614Mar 3, 2021Nov 16, 2021Aug 1, 2022Apr 17, 2023Dec 31, 2023Sep 14, 2024May 31, 2025
Updated 8mo ago

Evaluation results

MethodLinks
Semi-SSTBackbone=ViT-Small, Pre-training=DINO2025.052.42
Super-SSTBackbone=ViT-Small, Pre-training=DINO2025.053.37
FixMatchBackbone=WideResnet-28-2, mu=7, shared_codebase=true2021.044.55
FlexMatch2025.054.61
SequenceMatchBackbone=Wide-ResNet-28-2, #LABEL=2502023.104.75
DP-SSLTraining Labels=2502022.054.78
Dash (CTA)Training Labels=2502022.054.78
FixMatchlabeled samples=2502021.104.86
FixMatchBackbone=Wide-ResNet-28-2, #LABEL=2502023.104.86
FreeMatchBackbone=Wide-ResNet-28-2, #LABEL=2502023.104.88
Dash2025.054.95
AdaMatch2025.054.97
FlexMatchlabeled samples=2502021.104.98
FlexMatchBackbone=Wide-ResNet-28-2, #LABEL=2502023.104.98
FixMatch+AKC+ARC#label=2502021.034.99
FixMatch#label=2502021.035.04
FixMatch (CTA)Training Labels=2502022.055.07
SemCoBackbone=WideResnet-28-2, mu=7, shared_codebase=true2021.045.12
SimMatch2025.055.13
UDAlabeled samples=2502021.105.16
UDABackbone=Wide-ResNet-28-2, #LABEL=2502023.105.16
DASHBackbone=Wide-ResNet-28-2, #LABEL=2502023.105.16
CoMatch2025.055.16
FixMatch2025.055.23
CRMatch2025.055.35
SimMatchV22025.055.36
ReMixMatchTraining Labels=2502022.055.44
DoubleMatchTraining Labels=250, Evaluation Protocol=min2022.055.56
DoubleMatchTraining Labels=250, Evaluation Protocol=last 202022.055.72
MPLBackbone=Wide-ResNet-28-2, #LABEL=2502023.105.76
FixMatchBackbone=WideResnet-28-2, mu=3, shared_codebase=true2021.045.78
SemCoBackbone=WideResnet-28-2, mu=3, shared_codebase=true2021.045.87
ReMixMatchlabeled samples=2502021.106.3
ReMixMatchBackbone=Wide-ResNet-28-2, #LABEL=2502023.106.3
UDA2025.056.34
UDABackbone=WideResnet-28-22021.048.82
UDATraining Labels=2502022.058.82
MixMatch+AKC+ARC#label=2502021.038.95
MixMatch#label=2502021.0310.01
MixMatchTraining Labels=2502022.0511.05
MixMatchBackbone=WideResnet-28-2, shared_codebase=true2021.0411.29
Pseudo label#label=2502021.0312.92
ReMixMatch2025.0513
MixMatchlabeled samples=2502021.1013.63
MixMatchBackbone=Wide-ResNet-28-2, #LABEL=2502023.1013.63
AKC+ARC#label=2502021.0313.93
Mean teacher#label=2502021.0314.03
AKC#label=2502021.0314.49
ARC#label=2502021.0315.19
Supervised labeled#label=2502021.0315.92
PLCBBackbone=WideResnet-28-22021.0424.81
MixMatch2025.0528.87
Mean teacherBackbone=WideResnet-28-22021.0432.32
Mean Teacherlabeled samples=2502021.1037.46
MeanTeacherBackbone=Wide-ResNet-28-2, #LABEL=2502023.1037.46
VAT2025.0537.56
VATlabeled samples=2502021.1041.03
VATBackbone=Wide-ResNet-28-2, #LABEL=2502023.1041.03
Π-Modellabeled samples=2502021.1046.24
Pi ModelBackbone=Wide-ResNet-28-2, #LABEL=2502023.1046.24
Pseudo-Labelinglabeled samples=2502021.1046.49
Pseudo LabelBackbone=Wide-ResNet-28-2, #LABEL=2502023.1046.49
Pseudo-Labeling2025.0548.73
Pseudo-labelingBackbone=WideResnet-28-22021.0449.78
Mean Teacher2025.0551.12
Pi-Model2025.0556.24