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 5175 of 1751 papers

TitleStatusHype
A Survey on Domain Generalization for Medical Image AnalysisCode2
Adversarial Supervision Makes Layout-to-Image Diffusion Models ThriveCode2
Singer Identity Representation Learning using Self-Supervised TechniquesCode2
MMA: Multi-Modal Adapter for Vision-Language ModelsCode2
Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic SegmentationCode2
TransNeXt: Robust Foveal Visual Perception for Vision TransformersCode2
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target SimulationCode2
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuningCode2
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with RecommendationCode2
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image SegmentationCode2
Your Diffusion Model is Secretly a Zero-Shot ClassifierCode2
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling GeneralizationCode2
Generalized Parametric Contrastive LearningCode2
On-Device Domain GeneralizationCode2
T-NER: An All-Round Python Library for Transformer-based Named Entity RecognitionCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico ExperimentsCode2
Referring Image MattingCode2
Understanding The Robustness in Vision TransformersCode2
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timeCode2
BatchFormer: Learning to Explore Sample Relationships for Robust Representation LearningCode2
Pedestrian Detection: Domain Generalization, CNNs, Transformers and BeyondCode2
Learning to Prompt for Vision-Language ModelsCode2
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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