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 526–550 of 1751 papers

TitleStatusHype
Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations—0
Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization—0
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools—0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis—0
ConStyX: Content Style Augmentation for Generalizable Medical Image SegmentationCode0
RoCA: Robust Cross-Domain End-to-End Autonomous Driving—0
Harmonizing and Merging Source Models for CLIP-based Domain Generalization—0
FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient MatchingCode0
SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis—0
Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations—0
On Inverse Problems, Parameter Estimation, and Domain Generalization—0
UniPTMs: The First Unified Multi-type PTM Site Prediction Model via Master-Slave Architecture-Based Multi-Stage Fusion Strategy and Hierarchical Contrastive LossCode0
Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir ComputingCode0
Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction—0
A Flat Minima Perspective on Understanding Augmentations and Model Robustness—0
RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget—0
VUDG: A Dataset for Video Understanding Domain Generalization—0
Point-MoE: Towards Cross-Domain Generalization in 3D Semantic Segmentation via Mixture-of-Experts—0
Pseudo Multi-Source Domain Generalization: Bridging the Gap Between Single and Multi-Source Domain GeneralizationCode0
IRS: Incremental Relationship-guided Segmentation for Digital PathologyCode0
Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive LearningCode0
Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains—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