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 251275 of 1751 papers

TitleStatusHype
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-LabelingCode1
Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain GeneralizationCode1
An Information-theoretic Approach to Distribution ShiftsCode1
Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization AlgorithmsCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
DomainDrop: Suppressing Domain-Sensitive Channels for Domain GeneralizationCode1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Domain Generalization for Vision-based Driving Trajectory GenerationCode1
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution GeneralizationCode1
AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native SegmentationCode1
Domain-Adversarial Training of Neural NetworksCode1
ASPS: Augmented Segment Anything Model for Polyp SegmentationCode1
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image SegmentationCode1
Compound Text-Guided Prompt Tuning via Image-Adaptive CuesCode1
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose EstimationCode1
DIVA: Domain Invariant Variational AutoencodersCode1
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language GuidanceCode1
A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative LearningCode1
AADG: Automatic Augmentation for Domain Generalization on Retinal Image SegmentationCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image SegmentationCode1
SWAD: Domain Generalization by Seeking Flat MinimaCode1
Leveraging Vision-Language Models for Improving Domain Generalization in Image ClassificationCode1
Disentangling Masked Autoencoders for Unsupervised Domain GeneralizationCode1
A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic SegmentationCode1
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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