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 376400 of 1751 papers

TitleStatusHype
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Causality-inspired Single-source Domain Generalization for Medical Image SegmentationCode1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Semi-Supervised Domain Generalization with Evolving Intermediate DomainCode1
Masked Autoencoders Are Scalable Vision LearnersCode1
Learning Domain Invariant Representations in Goal-conditioned Block MDPsCode1
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain GeneralizationCode1
Learning Representations that Support Robust Transfer of PredictorsCode1
Exploiting Domain-Specific Features to Enhance Domain GeneralizationCode1
Invariant Language ModelingCode1
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student ModelsCode1
Learning Meta Pattern for Face Anti-SpoofingCode1
The Connection between Out-of-Distribution Generalization and Privacy of ML ModelsCode1
Domain-Specific Bias Filtering for Single Labeled Domain GeneralizationCode1
ResNet strikes back: An improved training procedure in timmCode1
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain DatasetsCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
Domain Generalization for Vision-based Driving Trajectory GenerationCode1
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from SimulationCode1
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image SegmentationCode1
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image SegmentationCode1
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language UnderstandingCode1
Fishr: Invariant Gradient Variances for Out-of-Distribution GeneralizationCode1
NAS-OoD: Neural Architecture Search for Out-of-Distribution GeneralizationCode1
Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage ClassificationCode1
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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