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 751–775 of 1751 papers

TitleStatusHype
Improving the Transferability of Adversarial Examples with Arbitrary Style TransferCode1
DomainDrop: Suppressing Domain-Sensitive Channels for Domain GeneralizationCode1
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack DetectionCode1
TDG: Text-guided Domain Generalization—0
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain GeneralizationCode1
BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation—0
ADRMX: Additive Disentanglement of Domain Features with Remix Loss—0
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object DetectionCode1
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals—0
Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous LabelsCode0
AFN: Adaptive Fusion Normalization via an Encoder-Decoder FrameworkCode1
Part-Aware Transformer for Generalizable Person Re-identificationCode1
DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data—0
Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and Context ExtractionCode0
A Novel Cross-Perturbation for Single Domain Generalization—0
Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting—0
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
Cross-Modal Concept Learning and Inference for Vision-Language Models—0
PromptStyler: Prompt-driven Style Generation for Source-free Domain GeneralizationCode1
NormAUG: Normalization-guided Augmentation for Domain Generalization—0
Phase Matching for Out-of-Distribution Generalization—0
Robust face anti-spoofing framework with Convolutional Vision Transformer—0
Cross Contrasting Feature Perturbation for Domain GeneralizationCode1
Flatness-Aware Minimization for Domain Generalization—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