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/ma7555/Augz
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/mabhay3420/Deep-Into-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/machinelearning-goettingen/EfficientNet-ModelScaling
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/maciejdomagala/DLWP_notes
https://github.com/mackitron/COVID19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahmoodlab/hest
https://github.com/mahmoodlab/hipt
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/makatx/YOLO_ResNet
https://github.com/makeyourownmaker/mixup
https://github.com/malin9402/2020-0221
https://github.com/manankshastri/ART
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/manoranjan03/PaperImplementations
https://github.com/maragori/DeepfakeForensics-v1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marcointrovigne/WeatherDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marload/ConvNets-TensorFlow2
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/marrlab/dinobloom
https://github.com/martinsbruveris/tensorflow-image-models
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/mashaan14/DANN-toy
https://github.com/mashaan14/VisionTransformer-MNIST
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/masoudnick/brain-tumor-mri-classification
https://github.com/matakshay/Neural_Image_Caption_Generator
https://github.com/matsuolab/t3a
https://github.com/mauriceweiler/MobiusCNNs
https://github.com/maxiao0234/ttga
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
https://github.com/maxwelltsai/DeepGalaxy
https://github.com/mbsariyildiz/resnet-pytorch
https://github.com/mbuet2ner/local-global-features-cnn
https://github.com/mdda/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mdmhriday/vision-transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/megvii-research/basecls/tree/main/zoo/public/effnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/megvii-research/basecls/tree/main/zoo/public/resnet
https://github.com/megvii-research/basecls/tree/main/zoo/public/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/megvii-research/basecls/tree/main/zoo/public/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/meowbutlerdev/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.