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 301–325 of 1751 papers

TitleStatusHype
Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification—0
Aligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification—0
In Search of Forgotten Domain Generalization—0
On the Generalization Properties of Deep Learning for Aircraft Fuel Flow Estimation Models—0
Adaptive High-Frequency Transformer for Diverse Wildlife Re-IdentificationCode1
QT-DoG: Quantization-aware Training for Domain GeneralizationCode1
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing—0
Fine-Tuning CLIP's Last Visual Projector: A Few-Shot CornucopiaCode1
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual ClassificationCode0
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable DiffusionCode2
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein EstimatorCode0
Parameter Competition Balancing for Model MergingCode1
Frequency-Based Federated Domain Generalization for Polyp SegmentationCode0
Identifying Knowledge Editing Types in Large Language ModelsCode0
Crafting Distribution Shifts for Validation and Training in Single Source Domain GeneralizationCode1
From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets—0
Exploring Language Model Generalization in Low-Resource Extractive QACode0
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain SchedulerCode0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
InterNet: Unsupervised Cross-modal Homography Estimation Based on Interleaved Modality Transfer and Self-supervised Homography PredictionCode1
Benchmarking Domain Generalization Algorithms in Computational PathologyCode0
AXCEL: Automated eXplainable Consistency Evaluation using LLMs—0
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization—0
Soft Segmented Randomization: Enhancing Domain Generalization in SAR-ATR for Synthetic-to-Measured—0
PromptTA: Prompt-driven Text Adapter for Source-free Domain GeneralizationCode1
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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