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 301350 of 1751 papers

TitleStatusHype
Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image AnalysisCode1
Domain Generalization Using Large Pretrained Models with Mixture-of-AdaptersCode1
Domain Generalization using Causal MatchingCode1
Deeper, Broader and Artier Domain GeneralizationCode1
Exploring Data Aggregation and Transformations to Generalize across Visual DomainsCode1
Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic RetinopathyCode1
Feature Alignment and Uniformity for Test Time AdaptationCode1
Contrastive Syn-to-Real GeneralizationCode1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias ReductionCode1
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency SpaceCode1
Domain Generalization via Entropy RegularizationCode1
AdaNPC: Exploring Non-Parametric Classifier for Test-Time AdaptationCode1
Domain generalization of 3D semantic segmentation in autonomous drivingCode1
SWAD: Domain Generalization by Seeking Flat MinimaCode1
Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal ShiftCode1
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack DetectionCode1
Domain Generalization via Gradient SurgeryCode1
Crafting Distribution Shifts for Validation and Training in Single Source Domain GeneralizationCode1
Collaborating Foundation Models for Domain Generalized Semantic SegmentationCode1
Selecting Data Augmentation for Simulating InterventionsCode1
Cross Contrasting Feature Perturbation for Domain GeneralizationCode1
Free Lunch for Domain Adversarial Training: Environment Label SmoothingCode1
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and BeyondCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center StudyCode1
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and TrackingCode1
Cross-domain Generalization for AMR ParsingCode1
Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual NormalizationCode1
Generalizable Model-agnostic Semantic Segmentation via Target-specific NormalizationCode1
Generalizable Sleep Staging via Multi-Level Domain AlignmentCode1
Augmenting Multi-Turn Text-to-SQL Datasets with Self-PlayCode1
A2XP: Towards Private Domain GeneralizationCode1
Domain Generalization for Vision-based Driving Trajectory GenerationCode1
Domain Generalization via Rationale InvarianceCode1
Dynamic Domain Adaptation for Efficient InferenceCode1
Generalize then Adapt: Source-Free Domain Adaptive Semantic SegmentationCode1
Domain Generalization by Mutual-Information Regularization with Pre-trained ModelsCode1
Domain Generalization by Learning and Removing Domain-specific FeaturesCode1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
DomainDrop: Suppressing Domain-Sensitive Channels for Domain GeneralizationCode1
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image SegmentationCode1
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part SegmentationCode1
Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI SegmentationCode1
Group-wise Inhibition based Feature Regularization for Robust ClassificationCode1
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural NetworksCode1
Domain Decorrelation with Potential Energy RankingCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
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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