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 101–125 of 1751 papers

TitleStatusHype
Domain Generalization through Attenuation of Domain-Specific InformationCode0
Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency AdaptationCode2
Decentralized Federated Domain Generalization with Style Sharing: A Formal Modeling and Convergence Analysis—0
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object DetectionCode2
Optimizing Specific and Shared Parameters for Efficient Parameter Tuning—0
Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic SegmentationCode2
Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization—0
Context-Aware Self-Adaptation for Domain Generalization—0
Generative Classifier for Domain Generalization—0
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation—0
A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content—0
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights—0
Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified?—0
Partial Transportability for Domain Generalization—0
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization—0
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous DomainsCode0
Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-SpoofingCode0
Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets—0
Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study—0
Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation—0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer—0
A Causal Perspective of Stock Prediction Models—0
Feature Modulation for Semi-Supervised Domain Generalization without Domain Labels—0
OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation TriadCode0
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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