Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 21
- 보존된 평가 기록
- 570
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HO4X/TSR_JetsonTX2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Halesu/4th-ML100Days
https://github.com/HaohanWang/ImageNet-Sketch
Learning Robust Global Representations by Penalizing Local Predictive Power
https://github.com/HaoyueBaiZJU/DecAug
https://github.com/HaoyueBaiZJU/NAS-OoD
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Harry-Muzart/harry-muzart.github.io
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Hazelsuko07/InstaHide
https://github.com/Hazqeel09/ellzaf_ml
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
https://github.com/Hong-Hyun-Seung/Mask_Classification
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/HyeonhoonLee/MAIC2021_Sleep
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HzcIrving/DeepLearning_PlayGround/tree/main/VIT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/IBM/IRM-games
Treatment Effect Estimation using Invariant Risk Minimization
https://github.com/IBM/NeuronAlignment
https://github.com/IMvision12/keras-vision-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/IamWangYunKai/DG-TrajGen
Domain Generalization for Vision-based Driving Trajectory Generation
https://github.com/IcarusWizard/MAE
https://github.com/IlyaDobrynin/GridMixup
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/IvanPy96/Clothes-images-classification
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/JContro/pytorch_projects
https://github.com/JHKim-snu/PGA
https://github.com/JHLee0513/Salt_detection_challenge
https://github.com/JIHOO97/Image-Classification-Competition
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/JONGSKY/paper
https://github.com/JacobM184/EfficientNet-for-Gun-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JaidedAI/EasyOCR
https://github.com/James-Gilbert-/medical-deep-learning
https://github.com/James-Gilbert-/pneumonia-detection
https://github.com/JanMarcelKezmann/Residual-Network-Architectures
https://github.com/JayPatwardhan/ResNet-PyTorch
https://github.com/JeansonYang/VGG16-MindSpore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Jeongyun-Lee-0423/Classification
https://github.com/Jeremy-lf/RT-DATR
https://github.com/JesonZhang822/Dogs-VS-Cats-Project
https://github.com/JeyesHan/P2LR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.