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 1376–1400 of 1751 papers

TitleStatusHype
Towards Generalizable Person Re-identification with a Bi-stream Generative Model—0
Self-Supervised Learning for Videos: A SurveyCode0
The Importance of Background Information for Out of Distribution Generalization—0
Domain Generalization via Selective Consistency Regularization for Time Series Classification—0
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization—0
Improving generalization by mimicking the human visual dietCode0
Slimmable Domain AdaptationCode0
INDIGO: Intrinsic Multimodality for Domain Generalization—0
Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization—0
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods—0
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification—0
Evolving Domain Generalization—0
Supporting Vision-Language Model Inference with Causality-pruning Knowledge Prompt—0
Test-time Batch Normalization—0
Generalizing to Evolving Domains with Latent Structure-Aware Sequential AutoencoderCode0
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering—0
Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation—0
InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy—0
Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation—0
EllSeg-Gen, towards Domain Generalization for head-mounted eyetrackingCode0
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion—0
Benchmarking Domain Generalization on EEG-based Emotion Recognition—0
MetaSets: Meta-Learning on Point Sets for Generalizable Representations—0
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces—0
Activation Regression for Continuous Domain Generalization with Applications to Crop ClassificationCode0
Show:102550
← PrevPage 56 of 71Next →
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