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Partial Domain Adaptation

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

Papers

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Showing 1–10 of 56 papers

TitleStatusHype
OneRing: A Simple Method for Source-free Open-partial Domain AdaptationCode1
A Balanced and Uncertainty-aware Approach for Partial Domain AdaptationCode1
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain AdaptationCode1
Learning to Transfer Examples for Partial Domain AdaptationCode1
Universal Domain Adaptation through Self SupervisionCode1
Improving Mini-batch Optimal Transport via Partial TransportationCode1
Adversarial Reweighting for Partial Domain AdaptationCode1
A Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypes—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
Adversarial Consistent Learning on Partial Domain Adaptation of PlantCLEF 2020 Challenge—0
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