SOTAVerified

Single-Source Domain Generalization

In this task a model is trained in a single source domain and then it is tested in a number of target domains

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–48 of 48 papers

TitleStatusHype
Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum SynthesisCode0
WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization—0
Aligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification—0
Center-aware Adversarial Augmentation for Single Domain Generalization—0
CNN Feature Map Augmentation for Single-Source Domain Generalization—0
Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations—0
DGSS : Domain Generalized Semantic Segmentation using Iterative Style Mining and Latent Representation Alignment—0
Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalization—0
Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis—0
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization—0
FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation—0
Frequency Decomposition to Tap the Potential of Single Domain for Generalization—0
HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalization—0
DRIFTS: Optimizing Domain Randomization with Synthetic Data and Weight Interpolation for Fetal Brain Tissue Segmentation—0
Meta-causal Learning for Single Domain Generalization—0
Meta Convolutional Neural Networks for Single Domain Generalization—0
Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data—0
Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification—0
PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization—0
Progressive Random Convolutions for Single Domain Generalization—0
Rethinking domain generalization in medical image segmentation: One image as one domain—0
TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction—0
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection—0
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All datasetsPACSDigits-five

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Crafting-Shifts(ResNet18)Accuracy70.37—Unverified
2MCL (ResNet18)Accuracy69.86—Unverified
3ProRandConv (ResNet18)Accuracy68.88—Unverified
4CADA (ResNet18)Accuracy68.41—Unverified
5ITTA (ResNet18)Accuracy68.4—Unverified
6XDED (ResNet18)Accuracy66.5—Unverified
7ABA (ResNet18)Accuracy66.36—Unverified
8GeoTexAug (ResNet18)Accuracy65—Unverified
9SagNet (ResNet18)Accuracy61.9—Unverified
10SelfReg (ResNet18)Accuracy59.59—Unverified
#ModelMetricClaimedVerifiedStatus
1Crafting-Shifts(LeNet)Accuracy82.61—Unverified
2ProRandConv (LeNet)Accuracy81.35—Unverified
3CADA (LeNet)Accuracy80.56—Unverified
4MCL (LeNet)Accuracy78.82—Unverified
5MetaCNN (LeNet)Accuracy78.76—Unverified
6ABA (LeNet)Accuracy76.72—Unverified
7L2D (LeNet)Accuracy74.46—Unverified