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 17261750 of 1751 papers

TitleStatusHype
Domain Information Control at Inference Time for Acoustic Scene ClassificationCode0
Domain-independent detection of known anomaliesCode0
Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data AugmentationCode0
RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus ImagesCode0
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in ClassificationCode0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior KnowledgeCode0
Consistency Regularization for Domain Generalization with Logit Attribution MatchingCode0
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster AssignmentCode0
Domain-Guided Weight Modulation for Semi-Supervised Domain GeneralizationCode0
ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater EnvironmentsCode0
Conditional entropy minimization principle for learning domain invariant representation featuresCode0
Robust AI-Generated Face Detection with Imbalanced DataCode0
Domain Generalized Object Detection for Remote Sensing ImagesCode0
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain GeneralizationCode0
Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality GapCode0
Domain Generalization with Vital Phase AugmentationCode0
When Neural Networks Fail to Generalize? A Model Sensitivity PerspectiveCode0
When Unseen Domain Generalization is Unnecessary? Rethinking Data AugmentationCode0
Robust Fine-Tuning of Vision-Language Models for Domain GeneralizationCode0
Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image SegmentationCode0
Domain Generalization with Fourier Transform and Soft ThresholdingCode0
Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language ModelsCode0
Domain Generalization with Correlated Style UncertaintyCode0
Domain Generalization via Semi-supervised Meta LearningCode0
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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