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 1451–1500 of 1751 papers

TitleStatusHype
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation—0
Meta-Learning for Domain Generalization in Semantic Parsing—0
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains—0
MetaReg: Towards Domain Generalization using Meta-Regularization—0
MetaSets：Meta-Learning on Point Sets for Generalizable Representations—0
MetaSets: Meta-Learning on Point Sets for Generalizable Representations—0
MetaTra: Meta-Learning for Generalized Trajectory Prediction in Unseen Domain—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach—0
Mitigate Domain Shift by Primary-Auxiliary Objectives Association for Generalizing Person ReID—0
Mitigating Both Covariate and Conditional Shift for Domain Generalization—0
Mitigating False Predictions In Unreasonable Body Regions—0
Improve Model Generalization and Robustness to Dataset Bias with Bias-regularized Learning and Domain-guided Augmentation—0
MitoDet: Simple and robust mitosis detection—0
Mitosis domain generalization in histopathology images -- The MIDOG challenge—0
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization—0
Mixstyle based Domain Generalization for Sound Event Detection with Heterogeneous Training Data—0
MixStyle Neural Networks for Domain Generalization and Adaptation—0
Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction—0
Mixture of Prompt Learning for Vision Language Models—0
Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation—0
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation—0
MLDGG: Meta-Learning for Domain Generalization on Graphs—0
MMoE: Robust Spoiler Detection with Multi-modal Information and Domain-aware Mixture-of-Experts—0
Modality-Agnostic Debiasing for Single Domain Generalization—0
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