Semi Supervised Image Classification On 1

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Top 1 Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Top 1 Accuracy	Paper Title	Repository
PAWS (ResNet-50)	66.5%	Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples	-
Rotation	-	S4L: Self-Supervised Semi-Supervised Learning	-
SimCLRv2 (ResNet-152 x3, SK)	74.9%	Big Self-Supervised Models are Strong Semi-Supervised Learners	-
VICREG (Resnet-50)	54.8%	VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning	-
Exemplar (joint training)	-	S4L: Self-Supervised Semi-Supervised Learning	-
SEER (RegNet10B)	62.4%	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision	-
SimCLRv2 distilled (ResNet-50 x2, SK)	75.9%	Big Self-Supervised Models are Strong Semi-Supervised Learners	-
Semi-ViT (ViT-Base)	71%	Semi-supervised Vision Transformers at Scale	-
Barlow Twins (ResNet-50)	55%	Barlow Twins: Self-Supervised Learning via Redundancy Reduction	-
DebiasPL (ResNet-50)	71.3%	Debiased Learning from Naturally Imbalanced Pseudo-Labels	-
SimMatchV2 (ResNet-50)	71.9%	SimMatchV2: Semi-Supervised Learning with Graph Consistency	-
PAWS (ResNet-50 2x)	69.6%	Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples	-
SimCLR (ResNet-50 4×)	63.0%	A Simple Framework for Contrastive Learning of Visual Representations	-
BYOL (ResNet-50)	53.2%	Bootstrap your own latent: A new approach to self-supervised Learning	-
CPC	-	Representation Learning with Contrastive Predictive Coding	-
SimCLRv2 distilled (ResNet-50)	73.9%	Big Self-Supervised Models are Strong Semi-Supervised Learners	-
SimCLR (ResNet-50 2×)	58.5%	A Simple Framework for Contrastive Learning of Visual Representations	-
CoMatch + EPASS (ResNet-50)	67.4%	Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector	-
Exemplar	-	S4L: Self-Supervised Semi-Supervised Learning	-
CoMatch (w. MoCo v2)	67.1%	CoMatch: Semi-supervised Learning with Contrastive Graph Regularization	-

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