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

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Showing 1101–1150 of 1751 papers

TitleStatusHype
A Novel Cross-Perturbation for Single Domain Generalization—0
Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting—0
Cross-Modal Concept Learning and Inference for Vision-Language Models—0
NormAUG: Normalization-guided Augmentation for Domain Generalization—0
Robust face anti-spoofing framework with Convolutional Vision Transformer—0
Phase Matching for Out-of-Distribution Generalization—0
Flatness-Aware Minimization for Domain Generalization—0
Adversarial Bayesian Augmentation for Single-Source Domain GeneralizationCode0
On the Fly Neural Style Smoothing for Risk-Averse Domain GeneralizationCode0
DISPEL: Domain Generalization via Domain-Specific Liberating—0
A Causal Framework to Unify Common Domain Generalization Approaches—0
Single Domain Generalization via Normalised Cross-correlation Based Convolutions—0
CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction—0
Probabilistic Test-Time Generalization by Variational Neighbor-Labeling—0
Domain Generalized Object Detection for Remote Sensing ImagesCode0
Multi-Similarity Contrastive Learning—0
Prompting Diffusion Representations for Cross-Domain Semantic SegmentationCode0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene SegmentationCode0
Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language ModelsCode0
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation—0
A Survey on Out-of-Distribution Evaluation of Neural NLP Models—0
Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction—0
Binary domain generalization for sparsifying binary neural networksCode0
Generalizable Metric Network for Cross-domain Person Re-identification—0
MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language ModelsCode0
Shape Guided Gradient Voting for Domain Generalization—0
FABLE : Fabric Anomaly Detection Automation ProcessCode0
Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models—0
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization—0
Modularity Trumps Invariance for Compositional RobustnessCode0
Domain Information Control at Inference Time for Acoustic Scene ClassificationCode0
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacyCode0
FedWon: Triumphing Multi-domain Federated Learning Without Normalization—0
Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization—0
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology—0
Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs—0
UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception DetectionCode0
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization—0
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification—0
Federated Domain Generalization: A Survey—0
Is Generative Modeling-based Stylization Necessary for Domain Adaptation in Regression Tasks?—0
SASMU: boost the performance of generalized recognition model using synthetic face dataset—0
Domain Generalization for Domain-Linked Classes—0
Signal Is Harder To Learn Than Bias: Debiasing with Focal LossCode0
CNN Feature Map Augmentation for Single-Source Domain Generalization—0
Domain Aligned Prefix Averaging for Domain Generalization in Abstractive SummarizationCode0
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization—0
Meta Adaptive Task Sampling for Few-Domain Generalization—0
HARD: Hard Augmentations for Robust Distillation—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