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 1426–1450 of 1751 papers

TitleStatusHype
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization—0
Less Forgetting for Better Generalization: Exploring Continual-learning Fine-tuning Methods for Speech Self-supervised Representations—0
Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization—0
Improving Domain Generalization with Domain Relations—0
LG-Gaze: Learning Geometry-aware Continuous Prompts for Language-Guided Gaze Estimation—0
Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation—0
LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models—0
Loss Function Learning for Domain Generalization by Implicit Gradient—0
Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading—0
MADG: Margin-based Adversarial Learning for Domain Generalization—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
Masked Audio Text Encoders are Effective Multi-Modal Rescorers—0
DRIFTS: Optimizing Domain Randomization with Synthetic Data and Weight Interpolation for Fetal Brain Tissue Segmentation—0
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing—0
Measuring and signing fairness as performance under multiple stakeholder distributions—0
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning—0
MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models—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
Meta-forests: Domain generalization on random forests with meta-learning—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—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