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 976–1000 of 1751 papers

TitleStatusHype
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes—0
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing—0
Generalizing to Unseen Domains: A Survey on Domain Generalization—0
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge—0
Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets—0
Generative Classifier for Domain Generalization—0
Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation—0
GLAD: Generalizable Tuning for Vision-Language Models—0
GLIDER: Grading LLM Interactions and Decisions using Explainable Ranking—0
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation—0
Gradient Estimation for Unseen Domain Risk Minimization with Pre-Trained Models—0
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models—0
Graph Augmentation for Cross Graph Domain Generalization—0
Guidance Not Obstruction: A Conjugate Consistent Enhanced Strategy for Domain Generalization—0
Handling Domain Shifts for Anomalous Sound Detection: A Review of DCASE-Related Work—0
HARD: Hard Augmentations for Robust Distillation—0
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation—0
Harmonizing and Merging Source Models for CLIP-based Domain Generalization—0
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization—0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization—0
Hospital-Agnostic Image Representation Learning in Digital Pathology—0
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
iBARLE: imBalance-Aware Room Layout Estimation—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