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 576600 of 1751 papers

TitleStatusHype
Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum SynthesisCode0
Meta Distribution Alignment for Generalizable Person Re-IdentificationCode0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Divergent Domains, Convergent Grading: Enhancing Generalization in Diabetic Retinopathy GradingCode0
Distribution-aware Noisy-label Crack SegmentationCode0
Bootstrap Generalization Ability from Loss Landscape PerspectiveCode0
Distillation-based fabric anomaly detectionCode0
Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D TransformersCode0
ConStyX: Content Style Augmentation for Generalizable Medical Image SegmentationCode0
Magnification Generalization for Histopathology Image EmbeddingCode0
Discriminative Radial Domain AdaptationCode0
Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UICode0
Binary domain generalization for sparsifying binary neural networksCode0
LFME: A Simple Framework for Learning from Multiple Experts in Domain GeneralizationCode0
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain ValidationCode0
Discrete Representations Strengthen Vision Transformer RobustnessCode0
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain GeneralizationCode0
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity RecognitionCode0
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented CalibrationCode0
Hallucinating Agnostic Images to Generalize Across DomainsCode0
Learning to Learn Single Domain GeneralizationCode0
Beyond Interpretability: The Gains of Feature Monosemanticity on Model RobustnessCode0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Learning to Generalize for Cross-domain QACode0
Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-raysCode0
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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