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 501–550 of 1751 papers

TitleStatusHype
Natural Adversarial ExamplesCode1
Invariant Risk MinimizationCode1
Learning Robust Global Representations by Penalizing Local Predictive PowerCode1
DIVA: Domain Invariant Variational AutoencodersCode1
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
Adversarial Training for Free!Code1
Making Convolutional Networks Shift-Invariant AgainCode1
A Closer Look at Few-shot ClassificationCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnessCode1
Two at Once: Enhancing Learning and Generalization Capacities via IBN-NetCode1
mixup: Beyond Empirical Risk MinimizationCode1
Learning to Generalize: Meta-Learning for Domain GeneralizationCode1
Deeper, Broader and Artier Domain GeneralizationCode1
Improved Regularization of Convolutional Neural Networks with CutoutCode1
Aggregated Residual Transformations for Deep Neural NetworksCode1
Deep CORAL: Correlation Alignment for Deep Domain AdaptationCode1
Domain Generalization for Object Recognition with Multi-task AutoencodersCode1
Domain-Adversarial Training of Neural NetworksCode1
Very Deep Convolutional Networks for Large-Scale Image RecognitionCode1
GLAD: Generalizable Tuning for Vision-Language Models—0
Simulate, Refocus and Ensemble: An Attention-Refocusing Scheme for Domain GeneralizationCode0
MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling—0
From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion—0
Integrated Structural Prompt Learning for Vision-Language Models—0
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