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 476–500 of 1751 papers

TitleStatusHype
A separability-based approach to quantifying generalization: which layer is best?Code0
Soft Prompt Generation for Domain GeneralizationCode1
VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain GeneralizationCode2
Transitive Vision-Language Prompt Learning for Domain Generalization—0
Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic ForgettingCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Mitigating False Predictions In Unreasonable Body Regions—0
Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularizationCode0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
Dynamic Proxy Domain Generalizes the Crowd Localization by Better Binary SegmentationCode0
IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained ModelsCode0
Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding—0
Multimodal 3D Object Detection on Unseen Domains—0
Single-temporal Supervised Remote Change Detection for Domain Generalization—0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint—0
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain GeneralizationCode0
DGMamba: Domain Generalization via Generalized State Space ModelCode1
PromptSync: Bridging Domain Gaps in Vision-Language Models through Class-Aware Prototype Alignment and Discrimination—0
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather—0
Soft-Prompting with Graph-of-Thought for Multi-modal Representation LearningCode0
Vision transformers in domain adaptation and domain generalization: a study of robustness—0
Domain Generalization through Meta-Learning: A Survey—0
Semantic Augmentation in Images using Language—0
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimalityCode0
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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