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 1301–1325 of 1751 papers

TitleStatusHype
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion—0
Generalizable Person Search on Open-world User-Generated Video Content—0
Generalizable Re-Identification from Videos with Cycle Association—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Accounting for Unobserved Confounding in Domain Generalization—0
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning—0
Generalization in Neural Networks: A Broad Survey—0
Generalization of feature embeddings transferred from different video anomaly detection domains—0
Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition—0
Generalizing across Temporal Domains with Koopman Operators—0
A Systematic Review of Generalization Research in Medical Image Classification—0
Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data—0
Adaptation and Generalization for Unknown Sensitive Factors of Variations—0
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation—0
Generalizing Nucleus Recognition Model in Multi-source Images via Pruning—0
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes—0
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing—0
Generalizing to Unseen Domains: A Survey on Domain Generalization—0
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge—0
Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets—0
Generative Classifier for Domain Generalization—0
Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation—0
GLAD: Generalizable Tuning for Vision-Language Models—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