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 13011350 of 1751 papers

TitleStatusHype
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers0
Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive FrameworkCode0
Pyramid Adversarial Training Improves ViT PerformanceCode0
TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification0
Towards Principled Disentanglement for Domain GeneralizationCode1
Calibrated Feature Decomposition for Generalizable Person Re-IdentificationCode1
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources0
Failure Modes of Domain Generalization AlgorithmsCode0
Confounder Identification-free Causal Visual Feature Learning0
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Causality-inspired Single-source Domain Generalization for Medical Image SegmentationCode1
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster AssignmentCode0
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Federated Learning with Domain Generalization0
Discrete Representations Strengthen Vision Transformer RobustnessCode0
Semi-Supervised Domain Generalization with Evolving Intermediate DomainCode1
Invariant Language Modeling0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization0
Masked Autoencoders Are Scalable Vision LearnersCode1
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding0
Learning Domain Invariant Representations in Goal-conditioned Block MDPsCode1
DEX: Domain Embedding Expansion for Generalized Person Re-identification0
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain GeneralizationCode1
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition0
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features0
Learning Representations that Support Robust Transfer of PredictorsCode1
Exploiting Domain-Specific Features to Enhance Domain GeneralizationCode1
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation0
Invariant Language ModelingCode1
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student ModelsCode1
Reappraising Domain Generalization in Neural Networks0
Domain generalization in deep learning for contrast-enhanced imaging0
Collaborative Semantic Aggregation and Calibration for Federated Domain GeneralizationCode0
Learning Meta Pattern for Face Anti-SpoofingCode1
Domain Generalization via Domain-based Covariance Minimization0
Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization0
Towards Data-Free Domain GeneralizationCode0
Scale Invariant Domain Generalization Image Recapture Detection0
The Connection between Out-of-Distribution Generalization and Privacy of ML ModelsCode1
Test-time Batch Statistics Calibration for Covariate Shift0
Dynamically Decoding Source Domain Knowledge for Domain Generalization0
Focus on the Common Good: Group Distributional Robustness FollowsCode0
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders0
Domain-Specific Bias Filtering for Single Labeled Domain GeneralizationCode1
Discussion on domain generalization in the cross-device speaker verification system0
ResNet strikes back: An improved training procedure in timmCode1
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization0
PDAML: A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition0
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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