Multiple-Source Adaptation with Domain Classifiers

08/25/2020
by   Corinna Cortes, et al.
12

We consider the multiple-source adaptation (MSA) problem and improve a previously proposed MSA solution, where accurate density estimation per domain is required to obtain favorable learning guarantees. In this work, we replace the difficult task of density estimation per domain with a much easier task of domain classification, and show that the two solutions are equivalent given the true densities and domain classifier, yet the newer approach benefits from more favorable guarantees when densities and domain classifier are estimated from finite samples. Our experiments with real-world applications demonstrate that the new discriminative MSA solution outperforms the previous solution with density estimation, as well as other domain adaptation baselines.

READ FULL TEXT

page 1

page 2

page 3

page 4

02/12/2008

M-decomposability, elliptical unimodal densities, and applications to clustering and kernel density estimation

Chia and Nakano (2009) introduced the concept of M-decomposability of pr...
05/14/2018

A One-Class Decision Tree Based on Kernel Density Estimation

One-Class Classification (OCC) is a domain of machine learning which ach...
01/11/2018

Some techniques in density estimation

Density estimation is an interdisciplinary topic at the intersection of ...
02/09/2018

Asymptotic nonequivalence of density estimation and Gaussian white noise for small densities

It is well-known that density estimation on the unit interval is asympto...
05/30/2019

AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

Given unpaired data from multiple domains, a key challenge is to efficie...
05/20/2018

Algorithms and Theory for Multiple-Source Adaptation

This work includes a number of novel contributions for the multiple-sour...
03/17/2017

Color Orchestra: Ordering Color Palettes for Interpolation and Prediction

Color theme or color palette can deeply influence the quality and the fe...