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 12261250 of 1751 papers

TitleStatusHype
Towards Domain Generalization in Object Detection0
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch RecognitionCode1
Causality Inspired Representation Learning for Domain GeneralizationCode1
FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization0
Compound Domain Generalization via Meta-Knowledge Encoding0
Domain-Generalized Textured Surface Anomaly Detection0
Deep Frequency Filtering for Domain Generalization0
Time-domain Generalization of Kron Reduction0
Improving Generalization in Federated Learning by Seeking Flat MinimaCode1
Feature Distribution Matching for Federated Domain GeneralizationCode0
A Broad Study of Pre-training for Domain Generalization and AdaptationCode1
Domain Generalization by Mutual-Information Regularization with Pre-trained ModelsCode1
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity RecognitionCode0
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine0
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and BeyondCode1
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain GeneralizationCode1
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness0
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering0
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part SegmentationCode1
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timeCode2
Conditional Prompt Learning for Vision-Language ModelsCode4
Domain Generalization via Shuffled Style Assembly for Face Anti-SpoofingCode1
Domain Generalization using Pretrained Models without Fine-tuning0
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain AdaptationCode0
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating0
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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