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

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Showing 1501–1525 of 1751 papers

TitleStatusHype
A Rate-Distortion Approach to Domain Generalization—0
LASSO: Latent Sub-spaces Orientation for Domain Generalization—0
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation—0
Variational Disentanglement for Domain Generalization—0
Shape-Biased Domain Generalization via Shock Graph Embeddings—0
HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image SegmentationCode0
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption—0
Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization—0
Improved RAMEN: Towards Domain Generalization for Visual Question AnsweringCode0
Barycentric-alignment and reconstruction loss minimization for domain generalizationCode0
MitoDet: Simple and robust mitosis detection—0
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge—0
Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection—0
Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge—0
Domain-Robust Mitotic Figure Detection with Style Transfer—0
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning—0
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge—0
Self-balanced Learning For Domain Generalization—0
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge—0
Discovering Spatial Relationships by Transformers for Domain Generalization—0
Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization—0
0.8% Nyquist computational ghost imaging via non-experimental deep learning—0
Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation—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