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 576–600 of 1751 papers

TitleStatusHype
Domain Generalization with Small Data—0
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object DetectionCode1
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach—0
A Survey on Domain Generalization for Medical Image AnalysisCode2
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?—0
Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality GapCode0
Causal Feature Selection for Responsible Machine Learning—0
SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning—0
Instance Paradigm Contrastive Learning for Domain Generalization—0
Understanding Domain Generalization: A Noise Robustness PerspectiveCode0
Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain GeneralizationCode1
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming—0
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization—0
Enhancing Evolving Domain Generalization through Dynamic Latent Representations—0
Adversarial Supervision Makes Layout-to-Image Diffusion Models ThriveCode2
Decoupled Prototype Learning for Reliable Test-Time Adaptation—0
Adapting Large Language Models for Document-Level Machine Translation—0
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations—0
Singer Identity Representation Learning using Self-Supervised TechniquesCode2
Any-Way Meta Learning—0
Meta-forests: Domain generalization on random forests with meta-learning—0
InvariantOODG: Learning Invariant Features of Point Clouds for Out-of-Distribution Generalization—0
VLLaVO: Mitigating Visual Gap through LLMsCode1
Preserving Silent Features for Domain Generalization—0
Multi-Source Collaborative Gradient Discrepancy Minimization for Federated 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