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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1251–1275 of 1751 papers

TitleStatusHype
Feature Alignment and Restoration for Domain Generalization and Adaptation—0
Feature-based Style Randomization for Domain Generalization—0
Feature Diversification and Adaptation for Federated Domain Generalization—0
Feature Modulation for Semi-Supervised Domain Generalization without Domain Labels—0
Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization—0
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment—0
FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis—0
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating—0
Federated Domain Generalization: A Survey—0
Federated Domain Generalization with Data-free On-server Matching Gradient—0
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training—0
Federated Learning with Domain Generalization—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding—0
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization—0
FedPartWhole: Federated domain generalization via consistent part-whole hierarchies—0
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation—0
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
Few-Shot Object Detection in Unseen Domains—0
FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation—0
Finding Diverse and Predictable Subgraphs for Graph Domain Generalization—0
On the Limitations of General Purpose Domain Generalisation Methods—0
Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset—0
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
Show:102550
← PrevPage 51 of 71Next →
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