Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 58
- 보존된 평가 기록
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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/yangsun22/tc-moa
https://github.com/yangyucheng000/MAE-2
https://github.com/yangyucheng000/University/tree/main/model-3/resnet
https://github.com/yangyucheng000/University/tree/main/model-3/superpoint
SuperPoint: Self-Supervised Interest Point Detection and Description
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/yeonsw/vkgdr
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/yistLin/dvector
https://github.com/yiyang-gt/feat2vec
https://github.com/yizhe-ang/AdaMatch-PyTorch
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://github.com/yjhong89/Domain-Adaptation
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
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/yongbowin/pkuseg-python_annotation
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
https://github.com/yongyupei/papers_with_examps/tree/main/mae
https://github.com/yoonkicho/bau
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/yoonkicho/pplr
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.