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 701–725 of 1751 papers

TitleStatusHype
GLAD: Generalizable Tuning for Vision-Language Models—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
Deep Domain-Adversarial Image Generation for Domain Generalisation—0
Decoupled Prototype Learning for Reliable Test-Time Adaptation—0
Decompose, Adjust, Compose: Effective Normalization by Playing with Frequency for Domain Generalization—0
Bag of Tricks for Out-of-Distribution Generalization—0
GLIDER: Grading LLM Interactions and Decisions using Explainable Ranking—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation—0
Decentralized Federated Domain Generalization with Style Sharing: A Formal Modeling and Convergence Analysis—0
A Causal Framework to Unify Common Domain Generalization Approaches—0
Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets—0
FedPartWhole: Federated domain generalization via consistent part-whole hierarchies—0
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization—0
Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding—0
DecAug: Augmenting HOI Detection via Decomposition—0
Generative Classifier for Domain Generalization—0
Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Federated Learning with Domain Generalization—0
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—0
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning—0
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training—0
Federated Domain Generalization with Data-free On-server Matching Gradient—0
Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations—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