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 576–600 of 1751 papers

TitleStatusHype
Domain-Class Correlation Decomposition for Generalizable Person Re-Identification—0
Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification—0
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs—0
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—0
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites—0
Domain Agnostic Few-shot Learning for Speaker Verification—0
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design—0
Domain Agnostic Conditional Invariant Predictions for Domain Generalization—0
A microservice-based framework for exploring data selection in cross-building knowledge transfer—0
FedPartWhole: Federated domain generalization via consistent part-whole hierarchies—0
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge—0
CADG: A Model Based on Cross Attention for Domain Generalization—0
Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition—0
Domain Adversarial Active Learning for Domain Generalization Classification—0
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization—0
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation—0
Domain Adaptive Ensemble Learning—0
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge—0
C^3DG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories—0
Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey—0
Domain2Vec: Deep Domain Generalization—0
DoGE: Domain Reweighting with Generalization Estimation—0
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Few-Shot Object Detection in Unseen Domains—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