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 13011325 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
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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