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

Showing 151200 of 1751 papers

TitleStatusHype
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
A Universal Representation Transformer Layer for Few-Shot Image ClassificationCode1
Consistency-guided Prompt Learning for Vision-Language ModelsCode1
Domain Generalization via Rationale InvarianceCode1
Domain Invariant Representation Learning with Domain Density TransformationsCode1
Domain Generalization Using Large Pretrained Models with Mixture-of-AdaptersCode1
Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal ShiftCode1
Domain Generalization via Entropy RegularizationCode1
Attention Consistency on Visual Corruptions for Single-Source Domain GeneralizationCode1
Compound Text-Guided Prompt Tuning via Image-Adaptive CuesCode1
A Bit More Bayesian: Domain-Invariant Learning with UncertaintyCode1
Collaborating Foundation Models for Domain Generalized Semantic SegmentationCode1
Attention Diversification for Domain GeneralizationCode1
Domain generalization of 3D semantic segmentation in autonomous drivingCode1
Domain Generalization using Causal MatchingCode1
Augmenting Multi-Turn Text-to-SQL Datasets with Self-PlayCode1
AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyCode1
A Broad Study of Pre-training for Domain Generalization and AdaptationCode1
Crafting Distribution Shifts for Validation and Training in Single Source Domain GeneralizationCode1
Domain Generalization via Gradient SurgeryCode1
Domain Generalization via Shuffled Style Assembly for Face Anti-SpoofingCode1
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part SegmentationCode1
DomainLab: A modular Python package for domain generalization in deep learningCode1
Unlocking Emergent Modularity in Large Language ModelsCode1
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies OthersCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense EncodersCode1
Domain-Specific Bias Filtering for Single Labeled Domain GeneralizationCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Cross Contrasting Feature Perturbation for Domain GeneralizationCode1
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?Code1
Domain Generalization for Medical Imaging Classification with Linear-Dependency RegularizationCode1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Domain Generalization for Object Recognition with Multi-task AutoencodersCode1
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
AdaNPC: Exploring Non-Parametric Classifier for Test-Time AdaptationCode1
Domain Generalization by Mutual-Information Regularization with Pre-trained ModelsCode1
Adversarial Training for Free!Code1
Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person MatchingCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
AdvST: Revisiting Data Augmentations for Single Domain GeneralizationCode1
Causality-inspired Single-source Domain Generalization for Medical Image SegmentationCode1
Benchmarking Distribution Shift in Tabular Data with TableShiftCode1
Which Invariance Should We Transfer? A Causal Minimax Learning ApproachCode1
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
A Fourier-based Framework for Domain GeneralizationCode1
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image SegmentationCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Show:102550
← PrevPage 4 of 36Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450Unverified
2VOLO-D5Accuracy - All Images57.2Unverified
3ConvNeXt-BAccuracy - All Images53.5Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1Unverified
9ViT-16/L-224Accuracy - All Images49Unverified
10ResNet-50 (gn)Accuracy - All Images48.9Unverified