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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1301–1325 of 1751 papers

TitleStatusHype
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization—0
UGformer for Robust Left Atrium and Scar Segmentation Across Scanners—0
Constrained Maximum Cross-Domain Likelihood for Domain Generalization—0
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport—0
Domain Generalization via Contrastive Causal Learning—0
TripleE: Easy Domain Generalization via Episodic ReplayCode0
Deep Spatial Domain GeneralizationCode0
Probing the Robustness of Pre-trained Language Models for Entity MatchingCode0
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive LearningCode0
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning—0
Domain Generalization -- A Causal Perspective—0
Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo LabelsCode0
Learning Gradient-based Mixup towards Flatter Minima for Domain Generalization—0
Generalizability of Adversarial Robustness Under Distribution Shifts—0
Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations—0
End-to-End Lyrics Recognition with Self-supervised Learning—0
A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection—0
Ani-GIFs: A benchmark dataset for domain generalization of action recognition from GIFs—0
AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization—0
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes—0
Bootstrap Generalization Ability from Loss Landscape PerspectiveCode0
Mitigating Both Covariate and Conditional Shift for Domain Generalization—0
Robust Ensemble Morph Detection with Domain Generalization—0
Enhance the Visual Representation via Discrete Adversarial TrainingCode0
A Continual Development Methodology for Large-scale Multitask Dynamic ML SystemsCode0
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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