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 12511275 of 1751 papers

TitleStatusHype
A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation0
Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring0
Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
Domain Generalization with Correlated Style UncertaintyCode0
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization0
Multi-View Knowledge Distillation from Crowd Annotations for Out-of-Domain Generalization0
PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment0
Calibration-Free Driver Drowsiness Classification based on Manifold-Level AugmentationCode0
Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds0
LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based RetrievalCode0
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion0
Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey0
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation0
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning0
Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning0
When Neural Networks Fail to Generalize? A Model Sensitivity PerspectiveCode0
Context-Aware Robust Fine-Tuning0
Domain Generalization for Robust Model-Based Offline Reinforcement Learning0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Domain generalization in fetal brain MRI segmentation \ multi-reconstruction augmentation0
Cross-domain Transfer of defect features in technical domains based on partial target data0
Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models0
Dynamic-Pix2Pix: Noise Injected cGAN for Modeling Input and Target Domain Joint Distributions with Limited Training DataCode0
HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization0
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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