Apr 28, 2018 · Our approach is based on formulating transfer from source to target as a problem of geometric mean metric learning on manifolds. Specifically, ...
Abstract. We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unla-.
A key strength of the proposed approach is that it enables integrating multiple sources of variation between source and target in a unified way, ...
Jan 23, 2019 · We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and ...
Bibliographic details on A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds.
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Aug 13, 2018 · Bibliographic details on A Unified Framework for Domain Adaptation using Metric Learning on Manifolds.
This repository contains code for the ECML 2018 paper titled "A Unified Framework for Domain Adaptation using Metric Learning on Manifolds".
We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unlabeled target ...
Jul 15, 2019 · Learning a discriminative model by shifting the distributions between source domain data and target domain data is known as Domain Adaptation or ...
Domain Adaptation with Structural Correspondence Learning. Conference Paper ... framework for reducing the distance between domains in a latent space for domain ...