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 1201–1225 of 1751 papers

TitleStatusHype
Domain Generalized Stereo Matching via Hierarchical Visual Transformation—0
Domain-Generalized Textured Surface Anomaly Detection—0
Domain-Invariant Disentangled Network for Generalizable Object Detection—0
Domain-invariant Feature Exploration for Domain Generalization—0
Towards Unsupervised Domain Generalization—0
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data—0
Domain-Robust Mitotic Figure Detection with Style Transfer—0
DomainVerse: A Benchmark Towards Real-World Distribution Shifts For Tuning-Free Adaptive Domain Generalization—0
DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data—0
DONOD: Robust and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning—0
Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis—0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
Dual Instruction Tuning with Large Language Models for Mathematical Reasoning—0
Dual-Path Stable Soft Prompt Generation for Domain Generalization—0
Dual Reweighting Domain Generalization for Face Presentation Attack Detection—0
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine—0
Dynamically Decoding Source Domain Knowledge for Domain Generalization—0
EAGLE: A Domain Generalization Framework for AI-generated Text Detection—0
ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models—0
EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training—0
Effective Inference-Free Retrieval for Learned Sparse Representations—0
Efficient LLM Context Distillation—0
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization—0
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation—0
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation—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