Signal Processing - November 2017 - 117

DEEP LEARNING FOR VISUAL UNDERSTANDING

Hemanth Venkateswara, Shayok Chakraborty,
and Sethuraman Panchanathan

Deep-Learning Systems
for Domain Adaptation in Computer Vision
Learning transferable feature representations

D

omain adaptation algorithms address the issue of transferring learning across computational models to adapt them to
data from different distributions. In recent years, research
in domain adaptation has been making great progress owing to
the advancements in deep learning. Deep neural networks have
demonstrated unrivaled success across multiple computer vision
applications, including transfer learning and domain adaptation.
This article outlines the latest research in domain adaptation
using deep neural networks. It begins with an introduction to the
concept of knowledge transfer in machine learning and the different paradigms of transfer learning. It provides a brief survey
of nondeep-learning techniques and organizes the rapidly growing research in domain adaptation based on deep learning. It also
highlights some drawbacks with the current state of research in
this area and offers directions for future research.

Introduction to domain adaptation

©ISTOCKPHOTO.COM/ZAPP2PHOTO

Digital Object Identifier 10.1109/MSP.2017.2740460
Date of publication: 13 November 2017

1053-5888/17©2017IEEE

Traditional machine-learning paradigms like supervised learning train statistical models to make predictions on unseen data
in the future. These models do not guarantee optimal performance if the test data are vastly different from the training
data. To reduce the effort involved in recollecting labeled data
and retraining a new model, knowledge transfer between tasks or
domains is desirable [1].
The concept of knowledge transfer and the need for adaptive machine-learning models is illustrated in the example of an
autonomous-driving car trained with daytime road-traffic data.
This car cannot be used to drive autonomously on the roads at
night since the light conditions are vastly different from the data
with which it was trained. Similarly, consider a car trained with
traffic data from sunny days. This car may not show the same
level of performance when it's snowing or raining; or a car trained
with road-traffic data from the United States will not work as
effectively on the streets of London, where the road-traffic rules
may vary with different signage, no turns on red, and driving on
the left side of the road being some of them. A self-driving car
will need to be trained with London street data (signs, traffic
rules, etc.) before it can be put to test on the streets of London.

IEEE SIGNAL PROCESSING MAGAZINE

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November 2017

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117


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Table of Contents for the Digital Edition of Signal Processing - November 2017

Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
Signal Processing - November 2017 - 4
Signal Processing - November 2017 - 5
Signal Processing - November 2017 - 6
Signal Processing - November 2017 - 7
Signal Processing - November 2017 - 8
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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