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 1001–1025 of 1751 papers

TitleStatusHype
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning—0
Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation—0
Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology—0
iMedic: Towards Smartphone-based Self-Auscultation Tool for AI-Powered Pediatric Respiratory Assessment—0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach—0
Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control—0
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training—0
Improving Domain Generalization on Gaze Estimation via Branch-out Auxiliary Regularization—0
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization—0
Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia—0
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space—0
HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalization—0
Improving mitosis detection on histopathology images using large vision-language models—0
Improving out-of-distribution generalization via multi-task self-supervised pretraining—0
Improving Semantic Segmentation via Self-Training—0
Improving Source-Free Target Adaptation with Vision Transformers Leveraging Domain Representation Images—0
Improving the Generalization of Meta-learning on Unseen Domains via Adversarial Shift—0
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy—0
Incorporating Supervised Domain Generalization into Data Augmentation—0
INDIGO: Intrinsic Multimodality for Domain Generalization—0
In Search of Forgotten Domain Generalization—0
In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation—0
Instance Paradigm Contrastive Learning 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