SOTAVerified

Domain Generalization

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

Papers

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Showing 1576–1600 of 1751 papers

TitleStatusHype
Unsupervised Visual Attention and Invariance for Reinforcement Learning—0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
Domain Generalization with MixStyle—0
Explainability-aided Domain Generalization for Image Classification—0
Confidence Calibration for Domain Generalization under Covariate Shift—0
Learning Domain Invariant Representations for Generalizable Person Re-Identification—0
Adaptive Methods for Real-World Domain Generalization—0
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation—0
Domain Generalization using Ensemble Learning—0
AI Fairness via Domain Adaptation—0
Uncertainty-guided Model Generalization to Unseen Domains—0
Domain Generalization: A Survey—0
Generalizing to Unseen Domains: A Survey on Domain Generalization—0
Domain Generalization via Inference-time Label-Preserving Target Projections—0
Neuron Coverage-Guided Domain Generalization—0
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization—0
Self-Domain Adaptation for Face Anti-Spoofing—0
A microservice-based framework for exploring data selection in cross-building knowledge transfer—0
On Calibration and Out-of-domain Generalization—0
Transferability of Neural Network Clinical De-identification Systems—0
Robust Domain-Free Domain Generalization with Class-aware Alignment—0
Robust White Matter Hyperintensity Segmentation on Unseen DomainCode0
Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to ImproveCode0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
Rethinking Domain Generalization Baselines—0
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99—Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6—Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9—Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6—Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4—Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3—Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2—Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97—Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450—Unverified
2VOLO-D5Accuracy - All Images57.2—Unverified
3ConvNeXt-BAccuracy - All Images53.5—Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7—Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5—Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7—Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6—Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1—Unverified
9ViT-16/L-224Accuracy - All Images49—Unverified
10ResNet-50 (gn)Accuracy - All Images48.9—Unverified