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 726750 of 1751 papers

TitleStatusHype
Dynamic-Pix2Pix: Noise Injected cGAN for Modeling Input and Target Domain Joint Distributions with Limited Training DataCode0
Activation Regression for Continuous Domain Generalization with Applications to Crop ClassificationCode0
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene SegmentationCode0
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity RecognitionCode0
OpenStance: Real-world Zero-shot Stance DetectionCode0
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and RestorationCode0
Assessing out-of-domain generalization for robust building damage detectionCode0
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain GeneralizationCode0
LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based RetrievalCode0
Language-Driven Dual Style Mixing for Single-Domain Generalized Object DetectionCode0
Joint covariate-alignment and concept-alignment: a framework for domain generalizationCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
IRS: Incremental Relationship-guided Segmentation for Digital PathologyCode0
Invariant Models for Causal Transfer LearningCode0
DomCLP: Domain-wise Contrastive Learning with Prototype Mixup for Unsupervised Domain GeneralizationCode0
DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text SummarizationCode0
Barycentric-alignment and reconstruction loss minimization for domain generalizationCode0
Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir ComputingCode0
Domain Separation NetworksCode0
A separability-based approach to quantifying generalization: which layer is best?Code0
Mixstyle-Entropy: Domain Generalization with Causal Intervention and PerturbationCode0
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain GeneralizationCode0
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary EnvironmentCode0
Conditional entropy minimization principle for learning domain invariant representation featuresCode0
Domain-Inspired Sharpness-Aware Minimization Under Domain ShiftsCode0
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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