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 1401–1425 of 1751 papers

TitleStatusHype
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
Feature Stylization and Domain-aware Contrastive Learning for Domain GeneralizationCode1
0.8% Nyquist computational ghost imaging via non-experimental deep learning—0
Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation—0
Domain Generalization via Gradient SurgeryCode1
Addressing materials' microstructure diversity using transfer learning—0
Adaptation and Generalization for Unknown Sensitive Factors of Variations—0
Improving the Generalization of Meta-learning on Unseen Domains via Adversarial Shift—0
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference—0
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation—0
Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains—0
Domain Generalization with Pseudo-Domain Label for Face Anti-Spoofing—0
Towards Unsupervised Domain Generalization—0
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation—0
Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains—0
Anatomy of Domain Shift Impact on U-Net Layers in MRI SegmentationCode1
On the Challenges of Open World Recognitionunder Shifting Visual DomainsCode1
Understanding the Limits of Unsupervised Domain Adaptation via Data PoisoningCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Generalizing Nucleus Recognition Model in Multi-source Images via Pruning—0
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation—0
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand SegmentationCode0
MixStyle Neural Networks for Domain Generalization and Adaptation—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