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/seermer/TensorFlow2-EfficientNetV2
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/seishinkikuchi/test
https://github.com/semi0612/cat_and_dog_vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/semskurto/APTOS
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/senthilva/CNN_CIFAR10_gradcam_cutout
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/seominseok0429/pytorch-warmup-cosine-lr
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/septmars/DL
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/serfaniane/siamese_person_re_id
https://github.com/serre-lab/gala_tpu
https://github.com/setharram/facenet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seujung/pytorch-vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/seunghwan1228/CNN_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seyrankhademi/ResNet_CIFAR10
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
https://github.com/shahrukhx01/ocr-test
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/shallowlearn/sportsreid
https://github.com/shao-chi/ImageCaption
https://github.com/sherdencooper/tricks-in-deeplearning
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/shijianjian/efficientnet-pytorch-3d
https://github.com/shinya7y/UniverseNet
https://github.com/shoaibahmed/CrossLayerPooling
https://github.com/shoji9x9/CIFAR-10-By-small-ResNet
https://github.com/shoji9x9/Fashion-MNIST-By-ResNet
https://github.com/shtnkgm/VisionFrameworkSample
https://github.com/sicara/easy-few-shot-learning
https://github.com/siddarth-c/FedGMA
https://github.com/siddharthbhonge/Neural_Style_transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sidify/resnet_focal_loss
https://github.com/simo-bat/Crack_detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/simon0987/Fast_FGSM
https://github.com/simongrest/farm-pin-crop-detection-challenge
https://github.com/simongrest/kaggle-freesound-audio-tagging-2019
https://github.com/singh-shakti94/Deep-Learning-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sitegui/ceci-nest-pas-un-chat
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sithu31296/semantic-segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.