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 1276–1300 of 1751 papers

TitleStatusHype
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data—0
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data—0
Single Domain Dynamic Generalization for Iris Presentation Attack Detection—0
Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary—0
Single Domain Generalization via Normalised Cross-correlation Based Convolutions—0
Single Domain Generalization with Adversarial Memory—0
Single Domain Generalization with Model-aware Parametric Batch-wise Mixup—0
Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariance—0
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification—0
Single-temporal Supervised Remote Change Detection for Domain Generalization—0
SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning—0
Soft Segmented Randomization: Enhancing Domain Generalization in SAR-ATR for Synthetic-to-Measured—0
SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning—0
Source data selection for out-of-domain generalization—0
StableFDG: Style and Attention Based Learning for Federated Domain Generalization—0
Stablizing Adversarial Invariance Induction by Discriminator Matching—0
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures—0
Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge—0
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization—0
Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring—0
Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains—0
Style-Guided Domain Adaptation for Face Presentation Attack Detection—0
Style Neophile: Constantly Seeking Novel Styles for Domain Generalization—0
StylePrompter: Enhancing Domain Generalization with Test-Time Style Priors—0
Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models—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