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Shape Guided Gradient Voting for Domain Generalization
Domain generalization aims to address the domain shift between training and testing data. To learn the domain invariant representations, the model is usually trained on multiple... -
TerraIncognita
The TerraIncognita dataset consists of 24,778 samples from four domains: painting, sketch, cartoon, and photo. -
Towards Unsupervised Domain Generalization
Unsupervised domain generalization (UDG) aims to learn generalizable models with unlabeled data and analyze the effects of pre-training on DG. -
PACS dataset
The dataset used in the paper is a large collection of small images, each representing a patch of a jigsaw puzzle. The patches are of the same size and orientation, and the goal... -
PACS, VLCS, Office-Home
The dataset used in the paper for domain generalization tasks. -
Wilds: A Benchmark of In-the-Wild Distribution Shifts
The dataset used in the paper is a collection of images for domain generalization tasks, including CIFAR-10-C, CIFAR-100-C, and Digit-DG. -
Domain Generalization for Object Recognition
Domain generalization for object recognition with multi-task autoencoders. -
Self-Balanced Domain Generalization
Domain generalization aims to learn a prediction model on multi-domain source data such that the model can generalize to a target domain with unknown statistics. -
Domain Generalization with MixStyle
The domain generalization with mixstyle dataset -
Deep Domain Generalization (DDG) dataset
The Deep Domain Generalization (DDG) dataset is a dataset for domain generalization. -
Domain Generalization via Jigsaw Puzzle (DGJP) dataset
The Domain Generalization via Jigsaw Puzzle (DGJP) dataset is a dataset for domain generalization using jigsaw puzzle. -
Deeper, Broader and Artier (DBA) dataset
The Deeper, Broader and Artier (DBA) dataset is a large-scale dataset for domain generalization. -
Deeper, broader and artier domain generalization
Deeper, broader and artier domain generalization. -
Cross Domain Generative Augmentation
The authors propose a novel data augmentation method called Cross Domain Generative Augmentation (CDGA) to reduce the estimation error of Empirical Risk Minimization (ERM) under...
