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 526–550 of 1751 papers

TitleStatusHype
Towards Generalizing to Unseen Domains with Few LabelsCode1
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction—0
Neural Markov Random Field for Stereo MatchingCode2
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose EstimationCode1
V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions—0
Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification—0
Single Domain Generalization for Crowd CountingCode2
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain GeneralizationCode0
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated ExpertsCode1
A Causal Inspired Early-Branching Structure for Domain GeneralizationCode0
Robust Synthetic-to-Real Transfer for Stereo MatchingCode2
Unknown Domain Inconsistency Minimization for Domain GeneralizationCode0
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning—0
Interpreting What Typical Fault Signals Look Like via Prototype-matching—0
On the Consideration of AI Openness: Can Good Intent Be Abused?—0
Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior KnowledgeCode0
Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain TransferCode1
A Study on Domain Generalization for Failure Detection through Human Reactions in HRICode0
Domain Adversarial Active Learning for Domain Generalization Classification—0
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation—0
CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model—0
Overcoming Data Inequality across Domains with Semi-Supervised Domain Generalization—0
MMoE: Robust Spoiler Detection with Multi-modal Information and Domain-aware Mixture-of-Experts—0
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated LearningCode0
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in ClassificationCode0
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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