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 1051–1075 of 1751 papers

TitleStatusHype
Kernel Embedding Approaches to Orbit Determination of Spacecraft Clusters—0
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization—0
LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization—0
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification—0
Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions—0
LASSO: Latent Sub-spaces Orientation for Domain Generalization—0
Latent Feature Disentanglement For Visual Domain Generalization—0
LawngNLI: a multigranular, long-premise NLI benchmark for evaluating models’ in-domain generalization from short to long contexts—0
Learning Attributes Equals Multi-Source Domain Generalization—0
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions—0
Learning Class and Domain Augmentations for Single-Source Open-Domain Generalization—0
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders—0
Learning Domain Invariant Representations for Generalizable Person Re-Identification—0
Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously—0
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization—0
Learning from Natural Language Explanations for Generalizable Entity Matching—0
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations—0
Learning Gradient-based Mixup towards Flatter Minima for Domain Generalization—0
Learning Instance-Specific Adaptation for Cross-Domain Segmentation—0
MePT: Multi-Representation Guided Prompt Tuning for Vision-Language Model—0
Meta Adaptive Task Sampling for Few-Domain Generalization—0
Meta-causal Learning for Single Domain Generalization—0
Meta Convolutional Neural Networks for Single Domain Generalization—0
Meta Curvature-Aware Minimization for Domain Generalization—0
MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single 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