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 1101–1125 of 1751 papers

TitleStatusHype
DGFamba: Learning Flow Factorized State Space for Visual Domain Generalization—0
DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control—0
DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification—0
DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding—0
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization—0
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation—0
DGSS : Domain Generalized Semantic Segmentation using Iterative Style Mining and Latent Representation Alignment—0
DIGIC: Domain Generalizable Imitation Learning by Causal Discovery—0
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning—0
Directional Domain Generalization—0
DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding—0
DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models—0
Discovering Spatial Relationships by Transformers for Domain Generalization—0
Discrepancy Minimization in Domain Generalization with Generative Nearest Neighbors—0
Discrepancy-Optimal Meta-Learning for Domain Generalization—0
Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization—0
Discussion on domain generalization in the cross-device speaker verification system—0
Disentangled Prompt Representation for Domain Generalization—0
DISPEL: Domain Generalization via Domain-Specific Liberating—0
Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation—0
DIVA: Domain Invariant Variational Autoencoder—0
Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
Diverse Target and Contribution Scheduling for Domain Generalization—0
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images—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