Domain Generalization On Imagenet C

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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
Mixer-B/8-SAM	48.9	When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
APR-SP + DeepAugment (ResNet-50)	-	Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
CAFormer-B36 (IN21K, 384)	-	MetaFormer Baselines for Vision
CAFormer-B36 (IN21K)	-	MetaFormer Baselines for Vision
FAN-L-Hybrid	67.7	Understanding The Robustness in Vision Transformers
GFNet-S	-	Global Filter Networks for Image Classification
MAE (ViT-H)	-	Masked Autoencoders Are Scalable Vision Learners
ResNet-50 (PushPull-Conv) + PRIME	69.4	PushPull-Net: Inhibition-driven ResNet robust to image corruptions
ConvNeXt-XL (Im21k) (augmentation overlap with ImageNet-C)	-	A ConvNet for the 2020s
FAN-L-Hybrid+STL	69.2	Fully Attentional Networks with Self-emerging Token Labeling
ConvFormer-B36	-	MetaFormer Baselines for Vision
QualNet (ResNeXt101)	-	Quality-Agnostic Image Recognition via Invertible Decoder
APR-SP (ResNet-50)	-	Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
DINOv2 (ViT-S/14, frozen model, linear eval)	-	DINOv2: Learning Robust Visual Features without Supervision
DiscreteViT	-	Discrete Representations Strengthen Vision Transformer Robustness
DiffAUD (Swin-Tiny)	61	Diffusion-Based Adaptation for Classification of Unknown Degraded Images
AugMix (ResNet-50)	-	AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
DINOv2 (ViT-g/14, frozen model, linear eval)	-	DINOv2: Learning Robust Visual Features without Supervision
DiffAUD (ResNet-50)	52.1	Diffusion-Based Adaptation for Classification of Unknown Degraded Images
GPaCo (ViT-L)	-	Generalized Parametric Contrastive Learning

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Domain Generalization On Imagenet C

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