https://github.com/federicopozzi33/MobileOne-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional 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/feedbackward/addro
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/felixgwu/img_classification_pk_pytorch
https://github.com/fengredrum/cnn-xla
https://github.com/filaPro/visda2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/filipmu/Kaggle-APTOS-2019-Blindness
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/filipradenovic/cnnimageretrieval
https://github.com/florenciopaucar/Covid-DataBoss
On the limits of cross-domain generalization in automated X-ray prediction
https://github.com/flowritecom/flow-merge
https://github.com/flytocc/ConvNeXt-paddle
https://github.com/flytocc/mae-paddle
https://github.com/fpsluozi/smd-smos
https://github.com/francislata/Sign-Language-Digits-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/frank-roesler/Image_Segmentation
https://github.com/frgfm/Holocron
https://github.com/fungtion/DSN
https://github.com/fzw97/GraphDA
https://github.com/gabrieletiboni/Domain-adaptation-on-PACS-dataset
https://github.com/gatheluck/Stronghold
https://github.com/gcr/torch-residual-networks
https://github.com/gentlezhu/shift-robust-gnns
https://github.com/ghif/mtae
Domain Generalization for Object Recognition with Multi-task Autoencoders
https://github.com/gimme1dollar/vision-transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ginn24/RefineDet-1
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/github-luffy/PFLD_68points_Pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gitlimlab/Representation-Learning-by-Learning-to-Count
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmum/dl-mo-2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/gnoses/ViT_examples
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/godwinrayanc/YOLOv1-Pytorch
https://github.com/gomezzz/MSMatch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/gomezzz/distmsmatch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/google-research/diffstride
https://github.com/google-research/t5x_retrieval
https://github.com/google/flax/tree/master/examples/imagenet
https://github.com/gopi-kampli/ImageClusteringDL
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.