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/raj-gupta1/Flower-Species-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/raj-gupta1/Tomato-leaves-disease-Predictor
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rajneeshaggarwal/google-efficientnet
https://github.com/ravi02512/efficientdet-keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rayanramoul/Visual-Transformer-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/realmichaelye/Skin-Lesions-Classifier-ResNet50
https://github.com/reiinakano/invariant-risk-minimization
https://github.com/researchmm/SiamDW
https://github.com/reyvaz/pneumothorax_detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/reyvaz/steel-defect-segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rgeirhos/texture-vs-shape
https://github.com/rhero12/Slic
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/richieyoum/Pneumonia-diagnosis
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ricky40403/BlurPool
https://github.com/rickyHong/tfRecord-Caltech256-repl2
https://github.com/rikichou/mmpose
https://github.com/rishabh-vij/Neural_Style_transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rishabmallick/Sign-language-live-predictor
https://github.com/rit-git/Snippext_public
https://github.com/rizqiamaliatuss/PotatoLeafDiseaseClassification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rizvansky/Internship-AI-Lab-IU-2020
On the limits of cross-domain generalization in automated X-ray prediction
https://github.com/rlagywns0213/cifar100_data_augmentation
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/rmwkwok/product_visual_search
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/robertofranceschi/Image-classification-on-Caltech101-using-CNNs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/roboflow/rf-detr
https://github.com/rohitgr7/tvmodels
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/roshankoirala/Fire_Detection_Model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rpryzant/proxy-a-distance
https://github.com/rtanno21609/AdaptiveNeuralTrees
https://github.com/ruinmessi/RFBNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ruiqirichard/eegeyenet-vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/rwchakra/exmap
https://github.com/rwightman/efficientnet-jax
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rwightman/gen-efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ry/tensorflow-resnet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.