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

Showing 301325 of 1751 papers

TitleStatusHype
Disentangling Masked Autoencoders for Unsupervised Domain GeneralizationCode1
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack DetectionCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Distilling Out-of-Distribution Robustness from Vision-Language Foundation ModelsCode1
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case GeneralizationCode1
Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic SegmentationCode1
Federated Unsupervised Domain Generalization using Global and Local Alignment of GradientsCode1
Contrastive Syn-to-Real GeneralizationCode1
FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain GeneralizationCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Deep Stable Learning for Out-Of-Distribution GeneralizationCode1
Federated Domain Generalization With Generalization AdjustmentCode1
Collaborating Foundation Models for Domain Generalized Semantic SegmentationCode1
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and BeyondCode1
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and TrackingCode1
A2XP: Towards Private Domain GeneralizationCode1
DEJA VU: Continual Model Generalization For Unseen DomainsCode1
Crafting Distribution Shifts for Validation and Training in Single Source Domain GeneralizationCode1
FETA: Towards Specializing Foundation Models for Expert Task ApplicationsCode1
DomainDrop: Suppressing Domain-Sensitive Channels for Domain GeneralizationCode1
Cross Contrasting Feature Perturbation for Domain GeneralizationCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
Domain Generalization by Learning and Removing Domain-specific FeaturesCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
Free Lunch for Domain Adversarial Training: Environment Label SmoothingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450Unverified
2VOLO-D5Accuracy - All Images57.2Unverified
3ConvNeXt-BAccuracy - All Images53.5Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1Unverified
9ViT-16/L-224Accuracy - All Images49Unverified
10ResNet-50 (gn)Accuracy - All Images48.9Unverified