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 951975 of 1751 papers

TitleStatusHype
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object DetectionCode1
Learning Optimal Features via Partial InvarianceCode0
DEJA VU: Continual Model Generalization For Unseen DomainsCode1
Causality-based Dual-Contrastive Learning Framework for Domain Generalization0
ManyDG: Many-domain Generalization for Healthcare ApplicationsCode1
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary EnvironmentCode0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning0
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL ParsingCode0
Modeling Uncertain Feature Representation for Domain GeneralizationCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection TrackCode0
1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification TrackCode0
Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology DatasetCode0
Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection0
Multi-View Action Recognition Using Contrastive LearningCode0
Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset0
Center-aware Adversarial Augmentation for Single Domain Generalization0
Towards Unsupervised Domain Generalization for Face Anti-Spoofing0
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
Environment Agnostic Representation for Visual Reinforcement LearningCode1
iDAG: Invariant DAG Searching for Domain GeneralizationCode1
DandelionNet: Domain Composition with Instance Adaptive Classification for Domain Generalization0
Task-aware Adaptive Learning for Cross-domain Few-shot Learning0
A Unified Framework for Robustness on Diverse Sampling Errors0
Federated Domain Generalization With Generalization AdjustmentCode1
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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