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 851–875 of 1751 papers

TitleStatusHype
Is Generative Modeling-based Stylization Necessary for Domain Adaptation in Regression Tasks?—0
HARD: Hard Augmentations for Robust Distillation—0
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation—0
DISPEL: Domain Generalization via Domain-Specific Liberating—0
Harmonizing and Merging Source Models for CLIP-based Domain Generalization—0
Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation—0
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization—0
A Language Anchor-Guided Method for Robust Noisy Domain Generalization—0
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation—0
Exploring the Impact of Synthetic Data for Aerial-view Human Detection—0
Adversarial Feature Learning under Accuracy Constraint for Domain Generalization—0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Cross-domain Transfer of defect features in technical domains based on partial target data—0
Invariant Batch Normalization for Multi-source Domain Generalization—0
Exploring and Utilizing Pattern Imbalance—0
Hospital-Agnostic Image Representation Learning in Digital Pathology—0
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization—0
Cross-Domain Generalization Through Memorization: A Study of Nearest Neighbors in Neural Duplicate Question Detection—0
Augmentation-based Domain Generalization for Semantic Segmentation—0
Invariant Causal Mechanisms through Distribution Matching—0
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—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