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

Showing 601625 of 1751 papers

TitleStatusHype
LFME: A Simple Framework for Learning from Multiple Experts in Domain GeneralizationCode0
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy ClassificationCode0
Benchmarking Domain Generalization Algorithms in Computational PathologyCode0
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain GeneralizationCode0
Learning to Learn Single Domain GeneralizationCode0
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot LearningCode0
Multi-View Action Recognition Using Contrastive LearningCode0
Probing the Robustness of Pre-trained Language Models for Entity MatchingCode0
Learning Spectral-Decomposed Tokens for Domain Generalized Semantic SegmentationCode0
Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain AdaptationCode0
Learning Semantic Role Labeling from Compatible Label SequencesCode0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
Learning Optimal Features via Partial InvarianceCode0
Deep Spatial Domain GeneralizationCode0
BatStyler: Advancing Multi-category Style Generation for Source-free Domain GeneralizationCode0
Deep Shape MatchingCode0
Generalizing to unseen domains via distribution matchingCode0
Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularizationCode0
Deep Multimodal Fusion for Generalizable Person Re-identificationCode0
Barycentric-alignment and reconstruction loss minimization for domain generalizationCode0
Deep Graph Laplacian Regularization for Robust Denoising of Real ImagesCode0
A Causal Inspired Early-Branching Structure for Domain GeneralizationCode0
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain GeneralizationCode0
Adversarial Style Augmentation for Domain Generalized Urban-Scene SegmentationCode0
Adversarial Style Augmentation for Domain GeneralizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450Unverified
2VOLO-D5Accuracy - All Images57.2Unverified
3ConvNeXt-BAccuracy - All Images53.5Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1Unverified
9ViT-16/L-224Accuracy - All Images49Unverified
10ResNet-50 (gn)Accuracy - All Images48.9Unverified