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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 11–20 of 56 papers

TitleStatusHype
Partial Distribution Matching via Partial Wasserstein Adversarial Networks—0
Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation ApproachCode0
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting—0
Robust Class-Conditional Distribution Alignment for Partial Domain Adaptation—0
A Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypes—0
MOT: Masked Optimal Transport for Partial Domain Adaptation—0
Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
Selective Partial Domain AdaptationCode0
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation—0
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