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/anguyen8/sam
https://github.com/anibali/dsnt-pose2d
https://github.com/aniket-patra-1998/FLOWER_RECOGNITION
https://github.com/aniquetahir/jax_ood
https://github.com/anjandeepsahni/face_classification
https://github.com/anjandeepsahni/face_recognition
https://github.com/ankit-vaghela30/Google-landmark-prediction
https://github.com/annstrange/breast-cancer-cnn
https://github.com/anoushkrit/Knowledge
https://github.com/antoinedemathelin/wann
https://github.com/anujdutt9/Artistic-Style-Transfer-using-Keras-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/anujinho/trident
https://github.com/anuraggoel70/Domain-Adversarial-Training-of-Neural-Networks
https://github.com/aobaruwa/TensorFlow-Projects
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/arahosu/musicslots
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ariG23498/mae-scalable-vision-learners
https://github.com/ariangc/breinchallenge
https://github.com/arkel23/PyTorch-Pretrained-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/armavox/deepcoral-pchelkin
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
https://github.com/armin-azh/3DefficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/arobey1/mbdg
https://github.com/asad-62/IVP-DNN
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/asahi417/DeepDomainAdaptation
https://github.com/asarigun/TransGAN
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras
https://github.com/ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ashushekar/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ashutosh1919/FaceGenerationStyleGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/asprenger/keras_fc_densenet
https://github.com/astoff/dog-breed-app
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/astoycos/Mini_Project2
https://github.com/asyml/vision-transformer-pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/automl/nes
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/autonomobil/SCDND-P2_Traffic-Sign-Classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.