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Domain Generalization On Imagenet C

Metrics

Top 1 Accuracy

Results

Performance results of various models on this benchmark

Paper Title
Mixer-B/8-SAM48.9When 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-Hybrid67.7Understanding 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) + PRIME69.4PushPull-Net: Inhibition-driven ResNet robust to image corruptions
ConvNeXt-XL (Im21k) (augmentation overlap with ImageNet-C)-A ConvNet for the 2020s
FAN-L-Hybrid+STL69.2Fully 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)61Diffusion-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)52.1Diffusion-Based Adaptation for Classification of Unknown Degraded Images
GPaCo (ViT-L)-Generalized Parametric Contrastive Learning
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Domain Generalization On Imagenet C | SOTA | HyperAI