28,175 research outputs found
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Domain Adaptive Transfer Learning for Fault Diagnosis
Thanks to digitization of industrial assets in fleets, the ambitious goal of
transferring fault diagnosis models fromone machine to the other has raised
great interest. Solving these domain adaptive transfer learning tasks has the
potential to save large efforts on manually labeling data and modifying models
for new machines in the same fleet. Although data-driven methods have shown
great potential in fault diagnosis applications, their ability to generalize on
new machines and new working conditions are limited because of their tendency
to overfit to the training set in reality. One promising solution to this
problem is to use domain adaptation techniques. It aims to improve model
performance on the target new machine. Inspired by its successful
implementation in computer vision, we introduced Domain-Adversarial Neural
Networks (DANN) to our context, along with two other popular methods existing
in previous fault diagnosis research. We then carefully justify the
applicability of these methods in realistic fault diagnosis settings, and offer
a unified experimental protocol for a fair comparison between domain adaptation
methods for fault diagnosis problems.Comment: Presented at 2019 Prognostics and System Health Management Conference
(PHM 2019) in Paris, Franc
- …