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 276300 of 1751 papers

TitleStatusHype
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous DrivingCode1
Deep Stable Learning for Out-Of-Distribution GeneralizationCode1
A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic SegmentationCode1
Deep Learning for Face Anti-Spoofing: A SurveyCode1
Deep Learning for Hate Speech Detection: A Comparative StudyCode1
Feature Alignment and Uniformity for Test Time AdaptationCode1
Federated Domain Generalization for Image Recognition via Cross-Client Style TransferCode1
Feature Stylization and Domain-aware Contrastive Learning for Domain GeneralizationCode1
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
Compound Text-Guided Prompt Tuning via Image-Adaptive CuesCode1
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency SpaceCode1
Deep CORAL: Correlation Alignment for Deep Domain AdaptationCode1
AADG: Automatic Augmentation for Domain Generalization on Retinal Image SegmentationCode1
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language GuidanceCode1
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image SegmentationCode1
Exploring Data Aggregation and Transformations to Generalize across Visual DomainsCode1
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose EstimationCode1
Deeper, Broader and Artier Domain GeneralizationCode1
Consistency-guided Prompt Learning for Vision-Language ModelsCode1
DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity RecognitionCode1
DEJA VU: Continual Model Generalization For Unseen DomainsCode1
Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic RetinopathyCode1
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time AdaptationCode1
AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native SegmentationCode1
Collaborating Foundation Models for Domain Generalized Semantic SegmentationCode1
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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