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
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image SegmentationCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution GeneralizationCode1
Frequency-mixed Single-source Domain Generalization for Medical Image SegmentationCode1
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object DetectionCode1
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image SegmentationCode1
Contrastive Syn-to-Real GeneralizationCode1
Gradient Matching for Domain GeneralizationCode1
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image SegmentationCode1
Collaborating Foundation Models for Domain Generalized Semantic SegmentationCode1
AdaNPC: Exploring Non-Parametric Classifier for Test-Time AdaptationCode1
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and BeyondCode1
DomainDrop: Suppressing Domain-Sensitive Channels for Domain GeneralizationCode1
Selecting Data Augmentation for Simulating InterventionsCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnessCode1
Crafting Distribution Shifts for Validation and Training in Single Source Domain GeneralizationCode1
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and TrackingCode1
Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person MatchingCode1
Cross Contrasting Feature Perturbation for Domain GeneralizationCode1
DomainLab: A modular Python package for domain generalization in deep learningCode1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Cross-Domain Few-Shot Classification via Learned Feature-Wise TransformationCode1
Attention Diversification for Domain GeneralizationCode1
Cross-domain Generalization for AMR ParsingCode1
Improving the Generalizability of Depression Detection by Leveraging Clinical QuestionnairesCode1
A2XP: Towards Private Domain GeneralizationCode1
Domain Generalization for Vision-based Driving Trajectory GenerationCode1
Augmenting Multi-Turn Text-to-SQL Datasets with Self-PlayCode1
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image SegmentationCode1
AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyCode1
Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal ShiftCode1
A Broad Study of Pre-training for Domain Generalization and AdaptationCode1
Domain Generalization via Entropy RegularizationCode1
A Universal Representation Transformer Layer for Few-Shot Image ClassificationCode1
Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain GeneralizationCode1
Generalizable Sleep Staging via Multi-Level Domain AlignmentCode1
Domain Generalization Using Large Pretrained Models with Mixture-of-AdaptersCode1
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching NetworksCode1
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part SegmentationCode1
Domain Generalization via Gradient SurgeryCode1
Domain Generalization via Rationale InvarianceCode1
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural NetworksCode1
Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and EvaluationCode1
Deep Stable Learning for Out-Of-Distribution GeneralizationCode1
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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