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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–625 of 1751 papers

TitleStatusHype
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
FMSG-JLESS Submission for DCASE 2024 Task4 on Sound Event Detection with Heterogeneous Training Dataset and Potentially Missing Labels—0
Generalizability of Adversarial Robustness Under Distribution Shifts—0
All in One Framework for Multimodal Re-identification in the Wild—0
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization—0
DocuT5: Seq2seq SQL Generation with Table Documentation—0
Diversity Boosted Learning for Domain Generalization with Large Number of Domains—0
Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations—0
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images—0
Diverse Target and Contribution Scheduling for Domain Generalization—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
Brain-Inspired Online Adaptation for Remote Sensing with Spiking Neural Network—0
Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification—0
Boundless Across Domains: A New Paradigm of Adaptive Feature and Cross-Attention for Domain Generalization in Medical Image Segmentation—0
Aligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification—0
DIVA: Domain Invariant Variational Autoencoder—0
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures—0
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization—0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
A Language Anchor-Guided Method for Robust Noisy Domain Generalization—0
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99—Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6—Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9—Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6—Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4—Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3—Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2—Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97—Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450—Unverified
2VOLO-D5Accuracy - All Images57.2—Unverified
3ConvNeXt-BAccuracy - All Images53.5—Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7—Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5—Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7—Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6—Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1—Unverified
9ViT-16/L-224Accuracy - All Images49—Unverified
10ResNet-50 (gn)Accuracy - All Images48.9—Unverified