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/woctezuma/steam-CLIP
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/wondonghyeon/protest-detection-violence-estimation
https://github.com/woongjoonchoi/DeepLearningPaper-Reproducing/tree/master/Vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wtjiang98/mmpose-gyl
https://github.com/wuqiyao20160118/mini_project-for-CIFAR10
https://github.com/wusaifei/HWCC_image_classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/wwwuyijia/mindspore_models-ResNeXt50
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/xch-liu/geom-tex-dg
https://github.com/xcmyz/Face-Binary-Classification
https://github.com/xcmyz/FaceDetection
https://github.com/xcmyz/Face_Binary_Classfication
https://github.com/xden2331/attentive_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/xhlulu/arxiv-assistant
https://github.com/xiaoyufenfei/LEDNet
https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters
https://github.com/xiuyu0000/new_papers_codes/tree/main/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/xiuyu0000/papers_with_examples/tree/main/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo
https://github.com/xiuyu0000/tutorials/tree/main/resnet
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/attention/OutlookAttention.py
https://github.com/xplip/pixel
https://github.com/xslidi/EfficientNets_ddl_apex
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/yahoo/open_nsfw
https://github.com/yakhyo/EfficientNet-PyTorch
https://github.com/yakhyo/gaze-estimation
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
https://github.com/yangarbiter/dp-dg
https://github.com/yangboz/TransferLearning4Dentist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yangorwell/NGPlus
https://github.com/yangsun22/tc-moa
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.