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/rynewu224/graphda
https://github.com/s-chh/pytorch-scratch-vision-transformer-vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/sagnik1511/Transfer-Learning-with-Python
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sahilunagar/VGG16-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/saic-fi/bayesian-prompt-learning
https://github.com/saikrishnadas/ResNet-Pytorch
https://github.com/sail-sg/poolformer
https://github.com/sailist/thexp-implement
https://github.com/saimj7/Action-Recognition-in-Real-Time
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/saloni1998/Cifar-10
https://github.com/sammyamajumdar/ResNet50
https://github.com/samotiian/CCSA
Unified Deep Supervised Domain Adaptation and Generalization
https://github.com/samsungsailmontreal/ghn3
https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sangHa0411/CIFAR100
https://github.com/sangHa0411/ImageNet
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/sangHa0411/ResNetCIFAR100
https://github.com/sangHa0411/VIT
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/sangdon/pac-ps-w
https://github.com/sanghyun-son/clustering-kernels
https://github.com/sankn123/Very-deep-convolutional-networks-for-large-scale-image-recognition-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sanoyo/dl-model
https://github.com/sarthakbaiswar/Artistic-Styling
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/say2sarwar/DeepAcid
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sayakpaul/ConvNeXt-TF
https://github.com/sayakpaul/FunMatch-Distillation
https://github.com/sayannath/ViT-Image-Classification
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/scarlettjiali/Tools_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/scpark20/universal-music-translation
https://github.com/scut-aitcm/Competitive-Inner-Imaging-SENet
https://github.com/scut-aitcm/CompetitiveSENet
https://github.com/seansoleyman/cifar10-resnet
https://github.com/seatizendoi/dinovdeau
https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.