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 526–550 of 1751 papers

TitleStatusHype
Domain generalization for retinal vessel segmentation via Hessian-based vector field—0
Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision—0
CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing—0
Exploring the Impact of Synthetic Data for Aerial-view Human Detection—0
Domain Generalization for Medical Image Analysis: A Review—0
Angular Visual Hardness—0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
Center-aware Adversarial Augmentation for Single Domain Generalization—0
Domain Generalization for In-Orbit 6D Pose Estimation—0
Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency—0
Domain Generalization for Domain-Linked Classes—0
Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification—0
CEC-Zero: Chinese Error Correction Solution Based on LLM—0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Causal Representation-Based Domain Generalization on Gaze Estimation—0
Domain Generalization for Activity Recognition via Adaptive Feature Fusion—0
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers—0
Domain Generalization for 6D Pose Estimation Through NeRF-based Image Synthesis—0
Domain Generalization Emerges from Dreaming—0
Domain Generalization Deep Graph Transformation—0
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation—0
AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization—0
Causality-inspired Latent Feature Augmentation for Single 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