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 1401–1425 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
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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