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 1551–1575 of 1751 papers

TitleStatusHype
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments—0
On Invariance Penalties for Risk Minimization—0
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs—0
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL—0
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization—0
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
Harmonization with Flow-based Causal InferenceCode0
Invariant Information Bottleneck for Domain Generalization—0
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
Adversarial Semantic Hallucination for Domain Generalized Semantic SegmentationCode0
Counterfactual Maximum Likelihood Estimation for Training Deep NetworksCode0
Feature-based Style Randomization for Domain Generalization—0
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain GeneralizationCode0
When Vision Transformers Outperform ResNets without Pre-training or Strong Data AugmentationsCode0
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms—0
Semi-Supervised Disparity Estimation with Deep Feature Reconstruction—0
Adversarially Adaptive Normalization for Single Domain Generalization—0
MetaSets：Meta-Learning on Point Sets for Generalizable Representations—0
On the benefits of representation regularization in invariance based domain generalization—0
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification—0
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Deep Domain Generalization with Feature-norm Network—0
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization—0
Open Domain Generalization with Domain-Augmented Meta-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