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 1326–1350 of 1751 papers

TitleStatusHype
Test-time Batch Normalization—0
Test-time Batch Statistics Calibration for Covariate Shift—0
Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization—0
Test-Time Domain Generalization for Face Anti-Spoofing—0
Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology—0
Test-time Loss Landscape Adaptation for Zero-Shot Generalization in Vision-Language Models—0
Test-Time Modality Generalization for Medical Image Segmentation—0
Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization—0
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?—0
Text-to-Image GAN with Pretrained Representations—0
The Importance of Background Information for Out of Distribution Generalization—0
The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization—0
TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction—0
Time-domain Generalization of Kron Reduction—0
Toward domain generalized pruning by scoring out-of-distribution importance—0
Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs—0
Towards a Better Evaluation of Out-of-Domain Generalization—0
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis—0
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning—0
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning—0
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View—0
Towards Domain Generalization in Object Detection—0
Towards Domain-Specific Features Disentanglement for Domain Generalization—0
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement—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