Domain Generalization
635 papers with code • 18 benchmarks • 25 datasets
The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
Benchmarks
These leaderboards are used to track progress in Domain Generalization
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Libraries
Use these libraries to find Domain Generalization models and implementationsDatasets
Fashion-MNIST
Office-Home
DomainNet
PACS
ImageNet-C
ImageNet-R
ImageNet-A
ImageNet-Sketch
Stylized ImageNet
WildDash
Latest papers with no code
Vision Transformers in Domain Adaptation and Generalization: A Study of Robustness
Motivated by the increased interest from the research community, our paper investigates the deployment of vision transformers in domain adaptation and domain generalization scenarios.
Domain Generalization through Meta-Learning: A Survey
Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications.
Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images
Fundus image segmentation on unseen domains is challenging, especially for the over-parameterized deep models trained on the small medical datasets.
Semantic Augmentation in Images using Language
Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning.
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy
We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models.
Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Matting
To address this challenge, we introduce a new learning paradigm, weakly semi-supervised human matting (WSSHM), which leverages a small amount of expensive matte labels and a large amount of budget-friendly segmentation labels, to save the annotation cost and resolve the domain generalization problem.
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
Central to our approach is modeling a unique prompt tailored for detecting unknown class samples, and to train this, we employ a readily accessible stable diffusion model, elegantly generating proxy images for the open class.
Domain Generalizable Person Search Using Unreal Dataset
Collecting and labeling real datasets to train the person search networks not only requires a lot of time and effort, but also accompanies privacy issues.
From Robustness to Improved Generalization and Calibration in Pre-trained Language Models
Enhancing generalization and uncertainty quantification in pre-trained language models (PLMs) is crucial for their effectiveness and reliability.
Test-Time Domain Generalization for Face Anti-Spoofing
Our method, consisting of Test-Time Style Projection (TTSP) and Diverse Style Shifts Simulation (DSSS), effectively projects the unseen data to the seen domain space.
