19 datasets found

The dataset used in this study for domain generalization in deep learning based mass detection in mammography.

The TerraIncognita dataset consists of 24,778 samples from four domains: painting, sketch, cartoon, and photo.

The DomainBed dataset is a benchmark for domain generalization, containing five multi-domain datasets (VLCS, PACS, OfficeHome, TerraIncognita, and DomainNet). The dataset is...

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...

The dataset used in the paper for domain generalization tasks.

The MIDOG Challenge was a competition on mitosis detection held at the International Conference on Medical Image Computing and Computer Assisted Intervention, 2021.

Domain generalization for object recognition with multi-task autoencoders.

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.

Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples.

DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control

The domain generalization with mixstyle dataset

The Deep Domain Generalization (DDG) dataset is a dataset for domain generalization.

The Domain Generalization via Jigsaw Puzzle (DGJP) dataset is a dataset for domain generalization using jigsaw puzzle.

The Deeper, Broader and Artier (DBA) dataset is a large-scale dataset for domain generalization.

Deeper, broader and artier domain generalization.

The DomainNet dataset is a benchmark for domain generalization, containing 100,000 images from 6 domains: Clipart, Infograph, Real, Painting, Quickdraw, and Sketch.

The OfficeHome dataset is a benchmark for domain generalization, containing 13,000 images from 6 domains: Clipart, Product, Real World, Art, Product, and Real World.

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...

The dataset used in the paper is a cross-domain dataset, consisting of six domains: Real, Painting, Sketch, Clipart, Infograph, and Quickdraw. Each domain contains 345 object...