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/yangyucheng000/University/tree/main/model-3/resnet
https://github.com/yangyucheng000/convnext/blob/main/convnext.py
https://github.com/yangyucheng000/gpu_autoaugment
https://github.com/yangyucheng000/mae
https://github.com/yangyucheng000/resnet_Ascend
https://github.com/yangyucheng000/resnet_GPU
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/yanlai00/bridge_data_imitation_learning
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
https://github.com/yanlai00/bridge_data_robot_infra
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
https://github.com/yaodongyu/projnorm
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/yaoshiang/MobileNetV2-CIFAR-Cleverhans
https://github.com/yaoxufeng/PCL-Proxy-based-Contrastive-Learning-for-Domain-Generalization
https://github.com/yashclone999/ResNet_MODEL
https://github.com/yaya-yns/tart
https://github.com/ycao5602/SAL
https://github.com/yczhang1017/SSD_resnet_pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yell/kaggle-camera
https://github.com/yibeiou/Advanced_Business_Intelligence
https://github.com/yifanzhang-pro/m-mae
https://github.com/yinboc/few-shot-meta-baseline
https://github.com/yjzhai-cs/vehicle-10
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ymittal23/PlayWithCifar
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/yongyupei/papers_with_examps/tree/main/mae
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/youngwoo-yoon/Practical-tips-for-lightweight-deep-learning
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/yqyao/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ys-zong/medfair
https://github.com/yudhisteer/Face-Recognition-with-Masks
https://github.com/yueatsprograms/ttt_cifar_release
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/yueatsprograms/ttt_imagenet_release
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/yuleiqin/capro
https://github.com/yuleiqin/fopro
https://github.com/yuvrajarora/CSCI5922_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yw0nam/DenseNet
https://github.com/yxgeee/MMT
https://github.com/yxgeee/SpCL
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.