SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 41–50 of 56 papers

TitleStatusHype
A Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypes—0
Associative Partial Domain Adaptation—0
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting—0
Bi-level Unbalanced Optimal Transport for Partial Domain Adaptation—0
Class Conditional Alignment for Partial Domain Adaptation—0
Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation—0
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation—0
Discriminative Clustering for Robust Unsupervised Domain Adaptation—0
Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation—0
Discriminative Partial Domain Adversarial Network—0
Show:102550
← PrevPage 5 of 6Next →

No leaderboard results yet.