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Multi-target Domain Adaptation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크: 0
평가표: 4
보존된 평가 기록: 14

The idea of Multi-target Domain Adaptation is to adapt a model from a single labelled source domain to multiple unlabelled target domains.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/lancerane/Adversarial-domain-adaptation

Unsupervised Domain Adaptation by Backpropagation

https://github.com/mashaan14/DANN-toy

Unsupervised Domain Adaptation by Backpropagation

https://github.com/sroutray/da-ganin

Unsupervised Domain Adaptation by Backpropagation

https://github.com/tachitachi/GradientReversal

Unsupervised Domain Adaptation by Backpropagation

https://github.com/tadeephuy/GradientReversal

Unsupervised Domain Adaptation by Backpropagation

https://github.com/theairbend3r/how-to-train-your-neural-net/blob/master/pytorch/computer_vision/domain_adaptation/gradient_reverse_layer.ipynb

Unsupervised Domain Adaptation by Backpropagation

https://github.com/thuml/Transfer-Learning-Library

Unsupervised Domain Adaptation by Backpropagation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.