Papers with Code 아카이브
Unsupervised Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 49
- 보존된 평가 기록
- 393
Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Duplums/bhb10k-dl-benchmark
https://github.com/E-DEEP/PapersReview
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
https://github.com/Elman295/Cross-Domain-Gradient-Discrepancy-Minimization-for-Unsupervised-Domain-Adaptation
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
https://github.com/Elman295/Maximum-Classifier-Discrepancy-for-Unsupervised-Domain-Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/Elman295/Unsupervised-Domain-Adaptation-by-Backpropagation-
https://github.com/EricAlcaide/MiniFold
https://github.com/EstherBear/implementation-of-pruning-filters
https://github.com/EverLookNeverSee/diag2model
https://github.com/Evgeneus/Graph-Domain-Adaptaion
https://github.com/FrancescoSaverioZuppichini/ResNet
https://github.com/Fujiki-Nakamura/ADDA.PyTorch
https://github.com/GJTNB/reading-memo
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
https://github.com/GKalliatakis/Keras-VGG16-places365
https://github.com/GLSS-UDA/GLSS-UDA.github.io
https://github.com/GT-RIPL/L2C
Learning to cluster in order to transfer across domains and tasks
https://github.com/Gabriel-Macias/robust_frcnn
Automatic adaptation of object detectors to new domains using self-training
https://github.com/GiovanniPasq/DA-Faster-RCNN
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/Gitikameher/PixelDA
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
https://github.com/Glp91/fire-detection
https://github.com/Graylab/deepH3-distances-orientations
https://github.com/HHTseng/video-classification
https://github.com/Halesu/4th-ML100Days
https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
https://github.com/HiGal/Domain-Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/IBM/NeuronAlignment
https://github.com/IMvision12/keras-vision-models
https://github.com/INZHAGY/WDA-Net
Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation
https://github.com/JContro/pytorch_projects
https://github.com/JHLee0513/Salt_detection_challenge
https://github.com/JONGSKY/paper
https://github.com/JaidedAI/EasyOCR
https://github.com/James-Gilbert-/medical-deep-learning
https://github.com/James-Gilbert-/pneumonia-detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.