https://github.com/wavinflaghxm/GroupSampling
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 13
- 평가표
- 58
- 보존된 평가 기록
- 391
Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/waychin-weiqin/itsa
https://github.com/weitianxin/awesome-distribution-shift
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift
https://github.com/weiwang2330/siamese
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/weixin-liang/metashift
https://github.com/wen227/Pokemon_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wenhui0206/MeanTeacher-DeepMedic
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
https://github.com/wenkehuang/marsfl
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning
https://github.com/wenxinxu/resnet-in-tensorflow
https://github.com/wenxinxu/resnet_in_tensorflow
https://github.com/wfwf10/feature-based-federated-transfer-learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wgchang/DSBN
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
https://github.com/whu-fanghan/EANet
EANet: Enhancing Alignment for Cross-Domain Person Re-identification
https://github.com/wildphoton/RandConv
Robust and Generalizable Visual Representation Learning via Random Convolutions
https://github.com/williamwang96/Almond-Recognition
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/willisk/GDH
https://github.com/winycg/MCL-OKD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wish44165/A-New-Perspective-for-Shuttlecock-Hitting-Event-Detection
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/wkostuch/wild-style
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wmkouw/covshift-l2reg
On Regularization Parameter Estimation under Covariate Shift
https://github.com/wmkouw/libTLDA
Target contrastive pessimistic risk for robust domain adaptation
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/wuyi2020/dorm
Domain Re-Modulation for Few-Shot Generative Domain Adaptation
https://github.com/wwwuyijia/mindspore_models-ResNeXt50
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/wxb589/Person_reID_baseline_pytorch-master
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/wzh-nlp/pltr
https://github.com/x-lance/neusym-rag
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.