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 1051–1075 of 1751 papers

TitleStatusHype
Constrained Maximum Cross-Domain Likelihood for Domain Generalization—0
Context is Environment—0
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights—0
Context-Aware Robust Fine-Tuning—0
Context-Aware Self-Adaptation for Domain Generalization—0
Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains—0
Continuous Domain Generalization—0
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms—0
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification—0
Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation—0
Contrastive Regression for Domain Adaptation on Gaze Estimation—0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology—0
ConvNLP: Image-based AI Text Detection—0
COPA: Comparing the Incomparable to Explore the Pareto Front—0
Correlation-aware Adversarial Domain Adaptation and Generalization—0
CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation—0
CRoF: CLIP-based Robust Few-shot Learning on Noisy Labels—0
Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization—0
Cross Domain Ensemble Distillation for Domain Generalization—0
Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data—0
Cross-Domain Generalization Through Memorization: A Study of Nearest Neighbors in Neural Duplicate Question Detection—0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
Cross-domain Transfer of defect features in technical domains based on partial target data—0
Crossing the Gap: Domain Generalization for Image Captioning—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