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 1476–1500 of 1751 papers

TitleStatusHype
MODA: Motion-Drift Augmentation for Inertial Human Motion Analysis—0
Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection—0
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization—0
ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization—0
Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data—0
MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning—0
MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling—0
Moving Beyond Navigation with Active Neural SLAM—0
Domain generalization in deep learning for contrast-enhanced imaging—0
Multi-channel Time Series Decomposition Network For Generalizable Sensor-Based Activity Recognition—0
Multi-component Image Translation for Deep Domain Generalization—0
Multi-Domain Adversarial Feature Generalization for Person Re-Identification—0
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment—0
MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization—0
Multimodal 3D Object Detection on Unseen Domains—0
Multiple data sources and domain generalization learning method for road surface defect classification—0
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification—0
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction—0
Multi-Similarity Contrastive Learning—0
Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification—0
Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization—0
Multi-Source Collaborative Style Augmentation and Domain-Invariant Learning for Federated Domain Generalization—0
Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection—0
Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea-Land Clutter Classification—0
Multi tasks RetinaNet for mitosis detection—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