https://github.com/NVIDIA/DeepLearningExamples
Aggregated Residual Transformations for Deep Neural Networks
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/NVIDIA/retinanet-examples
https://github.com/Nadavc220/DomainAdversarialTrainingOfNeuralNetworks
https://github.com/Nullius-2020/MAE-Paddle
https://github.com/OFRIN/Tensorflow_MixUp
https://github.com/OML-Team/open-metric-learning
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Oguzhanercan/Vision-Transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/Owais-Ansari/Unet3plus
https://github.com/P2333/Mixup-Inference
https://github.com/PPPrior/i3d-pytorch
https://github.com/PaddlePaddle/PASSL
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/PaddlePaddle/PLSC/tree/master/task/classification/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/PaddlePaddle/PaddleClas
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleOCR
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/PaddlePaddle/models
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/PatrickHua/SimpleMAE
https://github.com/Pealing/DDAN
Dual Distribution Alignment Network for Generalizable Person Re-Identification
https://github.com/PistonY/ResidualAttentionNetwork
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PotatoSpudowski/CactiNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/PsorTheDoctor/microarray-data
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/Pulkit-Khandelwal/mldg
Learning to Generalize: Meta-Learning for Domain Generalization
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/QQBrowserVideoSearch/CBVS-UniCLIP
https://github.com/RAF96/ifmo-2019-deep-learning-coursework
https://github.com/RPIDIAL/SAMix
https://github.com/Raghvender1205/ConvNeXt
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.