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 15511575 of 1751 papers

TitleStatusHype
Identifying Knowledge Editing Types in Large Language ModelsCode0
Beyond Interpretability: The Gains of Feature Monosemanticity on Model RobustnessCode0
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblesCode0
Domain-agnostic Question-Answering with Adversarial TrainingCode0
How Does Distribution Matching Help Domain Generalization: An Information-theoretic AnalysisCode0
Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous DrivingCode0
On the challenges of detecting MCI using EEG in the wildCode0
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved GeneralizationCode0
Benchmarking Domain Generalization Algorithms in Computational PathologyCode0
Slimmable Domain AdaptationCode0
BatStyler: Advancing Multi-category Style Generation for Source-free Domain GeneralizationCode0
On the Fly Neural Style Smoothing for Risk-Averse Domain GeneralizationCode0
Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to ImproveCode0
Soft-Prompting with Graph-of-Thought for Multi-modal Representation LearningCode0
Barycentric-alignment and reconstruction loss minimization for domain generalizationCode0
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous DomainsCode0
On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical ImagingCode0
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated LearningCode0
Doge Tickets: Uncovering Domain-general Language Models by Playing Lottery TicketsCode0
On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean FunctionsCode0
On the Universal Truthfulness Hyperplane Inside LLMsCode0
Heterogeneous Domain Generalization via Domain MixupCode0
ConStyX: Content Style Augmentation for Generalizable Medical Image SegmentationCode0
HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image SegmentationCode0
DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen DatasetsCode0
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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