Semi-supervised Image Classification

Benchmarks

Dataset Name	SOTA Method	Metric
ImageNet 1k (1%)	I-JEPA	Top-1 Acc77.3	49	4mo ago
CIFAR100-LT (test)	FixMatch + LA w/ ProCo	Accuracy0.613	48	4mo ago
Corel5k 10% (train)	GRaNDe	Accuracy95.77	40	2mo ago
Flowers 10% (train)	GRaNDe	Accuracy98.18	40	2mo ago
CUB200 (10% train)		Accuracy81.23	39	2mo ago
ImageNet (10% labeled)	PAWS	Top-1 Accuracy79	32	4mo ago
MNIST (test)	Ladder	Test Error0.0084	31	4mo ago
CIFAR100 (test)	FixMatch (STS)	Top-1 Acc77.92	23	4mo ago
CIFAR-10 standard (test)	FixMatch (STS)	Top-1 Accuracy95.94	22	4mo ago
ImageNet 1k (val)	SwAV	Top-5 Acc (1%)78.5	16	4mo ago
CIFAR-10 4000 labeled samples (train test)	S4L + ForkMerge+	Test Error13.1	14	4mo ago
CIFAR-100-LT (test)	FreeMatch w/ ABC	Error Rate0.456	12	4mo ago
CIFAR-10 LT (test)	FreeMatch w/ ABC	Error Rate13.9	12	4mo ago
ImageNet ILSVRC 2012 (1% labels)	BT	Top-1 Acc55	9	4mo ago
ImageNet 1% (val)	DATA (7G~8G)	Top-1 Acc49.5	8	4mo ago
ImageNet 1% labeled	PAWS	Top-1 Accuracy0.699	8	4mo ago
SVHN (test)	reSGHMC	Accuracy94.33	8	4mo ago
CIFAR10 (test)	reSGHMC	Accuracy84.67	8	4mo ago
Corel5k (test)	Multi-Feature Aggregation for GNNs	Accuracy98.53	6	1mo ago
CIFAR10 250 labels (2%) (test)	DebiasPL (w/ FixMatch)	Accuracy95.4	6	4mo ago
CIFAR10 40 labels (0.08%) (test)	DebiasPL (w/ FixMatch)	Accuracy94.6	6	4mo ago
CIFAR10-LT gamma=200, 1125 labels (10%) (test)	DebiasPL (w/ FixMatch)	Accuracy0.714	6	4mo ago
CIFAR10-LT gamma=100, 1244 labels (10%) (test)	DebiasPL (w/ FixMatch)	Accuracy79.2	6	4mo ago
CUB200 (test)	Multi-Feature Aggregation for GNNs	Accuracy (%)82.25	5	1mo ago
Flowers (test)	Multi-Feature Aggregation for GNNs	Accuracy99.73	5	1mo ago

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