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 1401–1450 of 1751 papers

TitleStatusHype
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
Learning Latent Spaces for Domain Generalization in Time Series Forecasting—0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision—0
Learning Robust Representations by Projecting Superficial Statistics Out—0
Learning Robust Spectral Dynamics for Temporal Domain Generalization—0
Learning to Augment via Implicit Differentiation for Domain Generalization—0
Learning to Generalize One Sample at a Time with Self-Supervision—0
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification—0
Learning to Learn Domain-invariant Parameters for Domain Generalization—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Learning to Learn with Variational Information Bottleneck for Domain Generalization—0
Learning to Optimize Domain Specific Normalization for Domain Generalization—0
Learning to Reason via Self-Iterative Process Feedback for Small Language Models—0
Probabilistic Test-Time Generalization by Variational Neighbor-Labeling—0
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
Show:102550
← PrevPage 29 of 36Next →
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