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 1476–1500 of 1751 papers

TitleStatusHype
Domain generalization in deep learning for contrast-enhanced imaging—0
Collaborative Semantic Aggregation and Calibration for Federated Domain GeneralizationCode0
Domain Generalization via Domain-based Covariance Minimization—0
Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization—0
Towards Data-Free Domain GeneralizationCode0
Scale Invariant Domain Generalization Image Recapture Detection—0
Test-time Batch Statistics Calibration for Covariate Shift—0
Dynamically Decoding Source Domain Knowledge for Domain Generalization—0
Focus on the Common Good: Group Distributional Robustness FollowsCode0
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders—0
Discussion on domain generalization in the cross-device speaker verification system—0
Loss Function Learning for Domain Generalization by Implicit Gradient—0
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification—0
Directional Domain Generalization—0
Cross Domain Ensemble Distillation for Domain Generalization—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—0
Latent Feature Disentanglement For Visual Domain Generalization—0
Towards Robust Domain Generalization in 2D Neural Audio Processing—0
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization—0
Discrepancy-Optimal Meta-Learning for Domain Generalization—0
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation—0
DaSeGAN: Domain Adaptation for Segmentation Tasks via Generative Adversarial Networks—0
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization—0
PDAML: A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition—0
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs—0
Show:102550
← PrevPage 60 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