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 1101–1125 of 1751 papers

TitleStatusHype
Generalization in Neural Networks: A Broad Survey—0
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification—0
Back-to-Bones: Rediscovering the Role of Backbones in Domain GeneralizationCode0
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems—0
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution—0
Multi tasks RetinaNet for mitosis detection—0
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures—0
OOD-Probe: A Neural Interpretation of Out-of-Domain Generalization—0
Bag of Tricks for Out-of-Distribution Generalization—0
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot ClassificationCode0
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue ClassificationCode0
A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction—0
GCISG: Guided Causal Invariant Learning for Improved Syn-to-real Generalization—0
A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks—0
Artifact-Based Domain Generalization of Skin Lesion ModelsCode0
ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization—0
Prompt Vision Transformer for Domain GeneralizationCode1
Domain-Specific Risk Minimization for Out-of-Distribution GeneralizationCode1
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives—0
MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization—0
Semantic Self-adaptation: Enhancing Generalization with a Single SampleCode1
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization—0
Adaptive Domain Generalization via Online Disagreement Minimization—0
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift—0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization—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