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

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Showing 776–800 of 1751 papers

TitleStatusHype
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating—0
DaSeGAN: Domain Adaptation for Segmentation Tasks via Generative Adversarial Networks—0
FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis—0
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization—0
DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition—0
Generalizable Metric Network for Cross-domain Person Re-identification—0
Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts—0
Autonomous Structural Memory Manipulation for Large Language Models Using Hierarchical Embedding Augmentation—0
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment—0
Generalizable Person Search on Open-world User-Generated Video Content—0
Generalizable Re-Identification from Videos with Cycle Association—0
DandelionNet: Domain Composition with Instance Adaptive Classification for Domain Generalization—0
Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization—0
Benchmarking Domain Generalization on EEG-based Emotion Recognition—0
Feature Modulation for Semi-Supervised Domain Generalization without Domain Labels—0
DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing—0
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning—0
Adversarially Adaptive Normalization for Single Domain Generalization—0
Generalization in Neural Networks: A Broad Survey—0
Feature Diversification and Adaptation for Federated Domain Generalization—0
Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections—0
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Improving Domain Generalization on Gaze Estimation via Branch-out Auxiliary Regularization—0
Feature-based Style Randomization for Domain Generalization—0
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99—Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6—Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9—Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6—Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4—Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3—Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2—Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97—Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450—Unverified
2VOLO-D5Accuracy - All Images57.2—Unverified
3ConvNeXt-BAccuracy - All Images53.5—Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7—Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5—Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7—Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6—Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1—Unverified
9ViT-16/L-224Accuracy - All Images49—Unverified
10ResNet-50 (gn)Accuracy - All Images48.9—Unverified