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
Efficient and Effective Augmentation Strategy for Adversarial TrainingCode1
Domain-Adversarial Training of Neural NetworksCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
Energy-Based Test Sample Adaptation for Domain GeneralizationCode1
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image SegmentationCode1
Environment Agnostic Representation for Visual Reinforcement LearningCode1
Contrastive Syn-to-Real GeneralizationCode1
ERM++: An Improved Baseline for Domain GeneralizationCode1
Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image AnalysisCode1
Domain Generalization by Learning and Removing Domain-specific FeaturesCode1
AdaNPC: Exploring Non-Parametric Classifier for Test-Time AdaptationCode1
Collaborating Foundation Models for Domain Generalized Semantic SegmentationCode1
Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic SegmentationCode1
Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding StatisticsCode1
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and BeyondCode1
Feature Stylization and Domain-aware Contrastive Learning for Domain GeneralizationCode1
Crafting Distribution Shifts for Validation and Training in Single Source Domain GeneralizationCode1
Cross-domain Generalization for AMR ParsingCode1
DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity RecognitionCode1
Cross Contrasting Feature Perturbation for Domain GeneralizationCode1
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack DetectionCode1
FETA: Towards Specializing Foundation Models for Expert Task ApplicationsCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and TrackingCode1
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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