Image Classification On Stl 10

评估指标

Percentage correct

评测结果

各个模型在此基准测试上的表现结果

		Paper Title
µ2Net+ (ViT-L/16)	99.64	A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
kNN-CLIP	99.6	Revisiting a kNN-based Image Classification System with High-capacity Storage
Wide-ResNet-101 (Spinal FC)	98.66	SpinalNet: Deep Neural Network with Gradual Input
CN(d=128)	98.45	Toward Understanding Supervised Representation Learning with RKHS and GAN
NSRL+CN(d=128)	98.36	Toward Understanding Supervised Representation Learning with RKHS and GAN
CN(d=64)	98.36	Toward Understanding Supervised Representation Learning with RKHS and GAN
NSRL+CN(d=32)	98.34	Toward Understanding Supervised Representation Learning with RKHS and GAN
NSRL+CN(d=64)	98.24	Toward Understanding Supervised Representation Learning with RKHS and GAN
CN(d=32)	98.17	Toward Understanding Supervised Representation Learning with RKHS and GAN
NAT-M4	97.9	Neural Architecture Transfer
NAT-M3	97.8	Neural Architecture Transfer
SEER (RegNet10B)	97.3	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
NAT-M2	97.2	Neural Architecture Transfer
iGPT-L	97.1	Generative Pretraining from Pixels
NAT-M1	96.7	Neural Architecture Transfer
EnAET	95.48	EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
VGG-19bn	95.44	SpinalNet: Deep Neural Network with Gradual Input
Diffusion Classifier (zero-shot)	95.4	Your Diffusion Model is Secretly a Zero-Shot Classifier
FixMatch (CTA)	94.83	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
ReMixMatch	94.77	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

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Image Classification On Stl 10

评估指标

评测结果

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Image Classification On Stl 10

评估指标

评测结果