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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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 891–900 of 1751 papers

Title	Date	Tasks	Status	Hype	Score
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization	Jun 30, 2021	Domain GeneralizationMachine Reading Comprehension	—Unverified	0	0
Mice to Machines: Neural Representations from Visual Cortex for Domain Generalization	May 11, 2025	Deep LearningDomain Generalization	—Unverified	0	0
0.8% Nyquist computational ghost imaging via non-experimental deep learning	Aug 17, 2021	3D Face Modelling	—Unverified	0	0
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design	Jul 15, 2024	Domain GeneralizationDrug Design	—Unverified	0	0
Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning	Jul 31, 2024	Contrastive LearningDomain Generalization	—Unverified	0	0
Synthesizing Text-to-SQL Data from Weak and Strong LLMs	Aug 6, 2024	Domain GeneralizationText to SQL	—Unverified	0	0
SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization	May 16, 2025	Domain Generalization	—Unverified	0	0
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection	Dec 9, 2021	3D Object DetectionData Augmentation	—Unverified	0	0
A Causal Framework to Unify Common Domain Generalization Approaches	Jul 13, 2023	Domain Generalization	—Unverified	0	0
A Causal Perspective of Stock Prediction Models	Mar 26, 2025	Causal DiscoveryDomain Generalization	—Unverified	0	0

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All datasets PACS VizWiz-Classification ImageNet-C Office-Home ImageNet-A ImageNet-R DomainNet VLCS TerraIncognita GTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-Sketch GTA5 to Cityscapes

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SIMPLE+	Average Accuracy	99	—	Unverified
2	PromptStyler (CLIP, ViT-L/14)	Average Accuracy	98.6	—	Unverified
3	GMDG (RegNetY-16GF, SWAD)	Average Accuracy	97.9	—	Unverified
4	D-Triplet(RegNetY-16GF)	Average Accuracy	97.6	—	Unverified
5	MoA (OpenCLIP, ViT-B/16)	Average Accuracy	97.4	—	Unverified
6	GMDG (e RegNetY-16GF)	Average Accuracy	97.3	—	Unverified
7	PromptStyler (CLIP, ViT-B/16)	Average Accuracy	97.2	—	Unverified
8	SPG (CLIP, ViT-B/16)	Average Accuracy	97	—	Unverified
9	CAR-FT (CLIP, ViT-B/16)	Average Accuracy	96.8	—	Unverified
10	MIRO (RegNetY-16GF, SWAD)	Average Accuracy	96.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ViT-8/B-224	Accuracy - Clean Images	450	—	Unverified
2	VOLO-D5	Accuracy - All Images	57.2	—	Unverified
3	ConvNeXt-B	Accuracy - All Images	53.5	—	Unverified
4	ResNeXt-101 32x16d	Accuracy - All Images	51.7	—	Unverified
5	EfficientNet-B8 (advprop+autoaug)	Accuracy - All Images	50.5	—	Unverified
6	EfficientNet-B7 (advprop+autoaug)	Accuracy - All Images	49.7	—	Unverified
7	EfficientNet-B6 (advprop+autoaug)	Accuracy - All Images	49.6	—	Unverified
8	EfficientNet-B5 (advprop+autoaug)	Accuracy - All Images	49.1	—	Unverified
9	ViT-16/L-224	Accuracy - All Images	49	—	Unverified
10	ResNet-50 (gn)	Accuracy - All Images	48.9	—	Unverified