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 9761000 of 1751 papers

TitleStatusHype
Domain Generalized Stereo Matching via Hierarchical Visual Transformation0
Exploring and Utilizing Pattern Imbalance0
Bi-Level Meta-Learning for Few-Shot Domain Generalization0
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet CategoriesCode0
Promoting Semantic Connectivity: Dual Nearest Neighbors Contrastive Learning for Unsupervised Domain Generalization0
Single Domain Generalization for LiDAR Semantic SegmentationCode1
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification0
Crossing the Gap: Domain Generalization for Image Captioning0
Discriminative Radial Domain AdaptationCode0
NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation0
A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation0
Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations0
Domain Generalization with Correlated Style UncertaintyCode0
UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation PipelineCode1
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization0
Multi-View Knowledge Distillation from Crowd Annotations for Out-of-Domain Generalization0
Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain GeneralizationCode1
PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment0
Calibration-Free Driver Drowsiness Classification based on Manifold-Level AugmentationCode0
Domain Generalization by Learning and Removing Domain-specific FeaturesCode1
Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds0
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies OthersCode1
Learning Domain Invariant Prompt for Vision-Language ModelsCode1
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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