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 1601–1625 of 1751 papers

TitleStatusHype
Magnification Generalization for Histopathology Image EmbeddingCode0
Invariant Batch Normalization for Multi-source Domain Generalization—0
A Simple Feature Augmentation for Domain Generalization—0
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains—0
Variational Invariant Learning for Bayesian Domain Generalization—0
Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation—0
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions—0
A Multi-Mode Modulator for Multi-Domain Few-Shot Classification—0
Uncertain Out-of-Domain Generalization—0
Domain-Free Adversarial Splitting for Domain Generalization—0
Domain-Invariant Disentangled Network for Generalizable Object Detection—0
TARA: Training and Representation Alteration for AI Fairness and Domain Generalization—0
Dataset of Random Relaxations for Crystal Structure Search of Li-Si System—0
Uncertainty-Aware Physically-Guided Proxy Tasks for Unseen Domain Face Anti-spoofing—0
Batch Normalization Embeddings for Deep Domain Generalization—0
Multi-Domain Adversarial Feature Generalization for Person Re-Identification—0
Cross-Domain Generalization Through Memorization: A Study of Nearest Neighbors in Neural Duplicate Question Detection—0
Assessing out-of-domain generalization for robust building damage detectionCode0
A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning—0
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity ClassificationCode0
Domain Generalization in Biosignal Classification—0
Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging—0
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain ValidationCode0
Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification—0
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—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