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 801–825 of 1751 papers

TitleStatusHype
DandelionNet: Domain Composition with Instance Adaptive Classification for Domain Generalization—0
Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization—0
Feature Modulation for Semi-Supervised Domain Generalization without Domain Labels—0
DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing—0
Adversarially Adaptive Normalization for Single Domain Generalization—0
BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation—0
Feature Diversification and Adaptation for Federated Domain Generalization—0
Feature-based Style Randomization for Domain Generalization—0
Adapting Large Language Models for Document-Level Machine Translation—0
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy—0
Incorporating Supervised Domain Generalization into Data Augmentation—0
A Systematic Review of Generalization Research in Medical Image Classification—0
In Search of Forgotten Domain Generalization—0
Adaptation and Generalization for Unknown Sensitive Factors of Variations—0
Feature Alignment and Restoration for Domain Generalization and Adaptation—0
Generalizing Nucleus Recognition Model in Multi-source Images via Pruning—0
Dataset of Random Relaxations for Crystal Structure Search of Li-Si System—0
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing—0
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation—0
Generalizing to Unseen Domains: A Survey on Domain Generalization—0
Directional Domain Generalization—0
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge—0
Adapting In-Domain Few-Shot Segmentation to New Domains without Retraining—0
Generative Classifier for Domain Generalization—0
Improving the Generalization of Meta-learning on Unseen Domains via Adversarial Shift—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