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 801825 of 1751 papers

TitleStatusHype
Information Subtraction: Learning Representations for Conditional EntropyCode0
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain GeneralizationCode0
Collaborative Semantic Aggregation and Calibration for Federated Domain GeneralizationCode0
Domain Generalization via Nuclear Norm RegularizationCode0
ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater EnvironmentsCode0
GeneralizeFormer: Layer-Adaptive Model Generation across Test-Time Distribution ShiftsCode0
Domain Generalization via Model-Agnostic Learning of Semantic FeaturesCode0
Domain generalization via invariant feature representationCode0
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot GeneralizationCode0
Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir ComputingCode0
A principled approach to model validation in domain generalizationCode0
Improved RAMEN: Towards Domain Generalization for Visual Question AnsweringCode0
Improving Domain Generalization by Learning without Forgetting: Application in Retail CheckoutCode0
Self-Supervised Learning for Videos: A SurveyCode0
Identifying Knowledge Editing Types in Large Language ModelsCode0
IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained ModelsCode0
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue ClassificationCode0
Domain Generalization Using a Mixture of Multiple Latent DomainsCode0
Domain Generalization through the Lens of Angular InvarianceCode0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved GeneralizationCode0
Beyond Interpretability: The Gains of Feature Monosemanticity on Model RobustnessCode0
How Does Distribution Matching Help Domain Generalization: An Information-theoretic AnalysisCode0
Domain Generalization through Attenuation of Domain-Specific InformationCode0
GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm SegmentationCode0
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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