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/linhduongtuan/Fruits_Vegetables_Classifier_WebApp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/liuch37/image-processing
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/liujiyuan13/MAE-code
https://github.com/liuruiyang98/Jittor-MLP
https://github.com/liuxingwt/CLS
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/liuyao12/ConvNets-PDE-perspective
https://github.com/lixihan/Resnet
https://github.com/lixixibj/forecasting-with-time-series-imaging
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lixixibj/tsImage-forecasting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ljy-hy/mentormix_pytorch
https://github.com/lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI
https://github.com/lnstadrum/fastaugment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/locuslab/fast_adversarial
https://github.com/lonnyzhang423/github-hot-hub
https://github.com/lornatang/googlenet-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lpirola13/flower-recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lpirola13/flower_recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ls-da3m0ns/Short-ResNet
https://github.com/lucidrains/denoising-diffusion-pytorch
https://github.com/lucidrains/vit-pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lucylow/salty-wet-man
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ludics/ViT-Retri
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lukas-blecher/LaTeX-OCR
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lukemelas/EfficientNet-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lukemelas/PyTorch-Pretrained-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/luuchung/cifar-100
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lvweiwolf/efficientdet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lxy000719/ResNet-50-pneumonia-classification
https://github.com/lyk19940625/MyRFBNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lyqcom/convnext
https://github.com/lyqcom/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lywang12/cupi-domain
https://github.com/lywang12/cuti-domain
https://github.com/lyxxn0414/test-data-generation
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/lzx6/deep-coral
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.