Papers with Code 아카이브

Partial Domain Adaptation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 5
보존된 평가 기록: 28

Partial Domain Adaptation is a transfer learning paradigm, which manages to transfer relevant knowledge from a large-scale source domain to a small-scale target domain.

Source: Deep Residual Correction Network for Partial Domain Adaptation

코드

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

https://github.com/Cavin-Lee/PAS

공식 코드

Unsupervised Domain Adaptation with Progressive Adaptation of Subspaces

https://github.com/VisionLearningGroup/DANCE

공식 코드

Universal Domain Adaptation through Self Supervision

https://github.com/albert0147/onering

공식 코드

OneRing: A Simple Method for Source-free Open-partial Domain Adaptation

https://github.com/cavin-lee/idsp

공식 코드

Partial Domain Adaptation without Domain Alignment

https://github.com/jihanyang/AFN

공식 코드

Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation

https://github.com/khainb/BoMb-OT

공식 코드

Improving Mini-batch Optimal Transport via Partial Transportation

https://github.com/mc-lan/sp-tcl

공식 코드

Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach

https://github.com/thuml/PADA

공식 코드

Partial Adversarial Domain Adaptation

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

공식 코드

From Big to Small: Adaptive Learning to Partial-Set Domains

https://github.com/thuml/Versatile-Domain-Adaptation

공식 코드

Minimum Class Confusion for Versatile Domain Adaptation

https://github.com/tim-learn/BA3US

공식 코드

A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation

https://github.com/tim-learn/SHOT

공식 코드

Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

https://github.com/ut-austin-data-science-group/mini-batch-ot

공식 코드

Improving Mini-batch Optimal Transport via Partial Transportation

https://github.com/wenqiwenqi1/DRCN

공식 코드

Deep Residual Correction Network for Partial Domain Adaptation

https://github.com/AlexisGuichemerreCode/survey_hist_wsol_sfda

Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

https://github.com/Claydon-Wang/OFTTA

Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

https://github.com/Pengxin-Guo/SPDA

https://github.com/Solacex/Domain-Consensus-Clustering

https://github.com/gpx333/spda

https://github.com/hellowangqian/scs-lp-pda

https://github.com/kevinmusgrave/pytorch-adapt

Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation

https://github.com/thuml/ETN

Learning to Transfer Examples for Partial Domain Adaptation

https://github.com/xjtu-xgu/adversarial-reweighting-for-partial-domain-adaptation

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