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 1226–1250 of 1751 papers

TitleStatusHype
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization—0
Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels—0
Enhancing Evolving Domain Generalization through Dynamic Latent Representations—0
Enhancing Representation Generalization in Authorship Identification—0
Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization—0
Invariance Principle Meets Vicinal Risk Minimization—0
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift—0
Evaluating and Enhancing Out-of-Domain Generalization of Task-Oriented Dialog Systems for Task Completion without Turn-level Dialog Annotations—0
Evaluating Embedding APIs for Information Retrieval—0
Evolving Domain Generalization—0
Exchanging Lessons Between Algorithmic Fairness and Domain Generalization—0
Explainability-aided Domain Generalization for Image Classification—0
Explainable Deep Classification Models for Domain Generalization—0
TextSleuth: Towards Explainable Tampered Text Detection—0
Explaining The Efficacy of Counterfactually Augmented Data—0
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation—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
Exploring and Utilizing Pattern Imbalance—0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Exploring the Impact of Synthetic Data for Aerial-view Human Detection—0
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods—0
FADE: Towards Fairness-aware Augmentation for Domain Generalization via Classifier-Guided Score-based Diffusion Models—0
Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging—0
FAMLP: A Frequency-Aware MLP-Like Architecture For 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