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 901–925 of 1751 papers

TitleStatusHype
Accelerating Chain-of-Thought Reasoning: When Goal-Gradient Importance Meets Dynamic Skipping—0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation—0
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs—0
A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection—0
A Cross-Scene Benchmark for Open-World Drone Active Tracking—0
Actions and Objects Pathways for Domain Adaptation in Video Question Answering—0
Activate and Reject: Towards Safe Domain Generalization under Category Shift—0
Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting—0
Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning—0
Adapting In-Domain Few-Shot Segmentation to New Domains without Retraining—0
Adapting Large Language Models for Document-Level Machine Translation—0
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation—0
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport—0
Adaptive Domain Generalization for Digital Pathology Images—0
Adaptive Domain Generalization via Online Disagreement Minimization—0
Adaptive Face Recognition Using Adversarial Information Network—0
Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images—0
Adaptive Methods for Real-World Domain Generalization—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation—0
AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction—0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
Addressing Artificial Intelligence Bias in Retinal Disease Diagnostics—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