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 21–30 of 56 papers

TitleStatusHype
Source Class Selection with Label Propagation for Partial Domain AdaptationCode0
Partial Domain Adaptation without Domain AlignmentCode0
Improving Mini-batch Optimal Transport via Partial TransportationCode1
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network—0
Partial Feature Selection and Alignment for Multi-Source Domain Adaptation—0
Domain Consensus Clustering for Universal Domain AdaptationCode0
On Evolving Attention Towards Domain Adaptation—0
Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation—0
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation—0
Adversarial Consistent Learning on Partial Domain Adaptation of PlantCLEF 2020 Challenge—0
Show:102550
← PrevPage 3 of 6Next →

No leaderboard results yet.