Signal Processing - November 2017 - 118

However, it would be expensive and time-consuming to acquire
such training data and retrain a new self-driving car from scratch,
especially considering the time and resources needed to train a
self-driving car. It is in these situations that domain adaptation
algorithms help to transfer the knowledge gained from learning
in one environment and reduce the training effort when adapting
the model to a new environment.
Variations in vision-based data can be attributed to multiple
causes, such as differences in image quality (resolution, brightness, occlusion, and color), changes in camera perspective, dissimilar backgrounds, and an inherent diversity of the samples
themselves. All of these can result in distribution mismatch
between training and test data. Distribution mismatch can also
arise when training and test data are from different modalities;
for example, standard color red, blue, green (RGB) image data
versus RGB-depth data as in [2]-[4], RGB data versus image
sketches [5], or RGB data versus paintings [6]. The authors in
[7] perform heterogeneous face recognition across near-infrared images, RGB images, and image sketches. Castrejon et al.
[8] introduce a procedure for multimodal domain adaptation
across RGB, sketches, clipart, and textual descriptions of indoor
scenes. Distribution mismatch can also be introduced when
there is a time lag between the capture of image instances [9].
In all of the aforementioned procedures, different domain adaptation techniques are employed to adapt computational models
across distributions.
Domain adaptation deals with knowledge transfer, where
knowledge from a source domain is transferred to a target
domain in the form of learned models and efficient feature representations. The data from the source and the target, although
similar, are from different distributions, for, e.g., U.S. street data
versus London street data. A machine-learning model trained on
the source data set is often adapted to the target data set. The
challenge for transfer of knowledge occurs when there are very
limited or no labeled data in the target domain, which makes it
hard to train models that need some form of supervision. This
section defines the problem of knowledge transfer, describes the
different transfer learning paradigms along with domain adaptation, and outlines the relevance of research in domain adaptation.

Problem definition
In a standard supervised learning setting, test data are sampled
from the same distribution as the training data. Therefore, trained
models can guarantee a level of performance. When test data
come from a distribution very different from training data, transfer of knowledge from the training domain is necessary to build
robust models. At the core of a transfer learning system is a computational model that retains knowledge from one or more tasks,
domains, or distributions and applies that knowledge to develop
an effective hypothesis for a new one [10]. Transfer learning is
often associated with domain adaptation; however, it is more elucidative to understand transfer learning as a broader paradigm
that encompasses multiple types of knowledge transfer [1], [10],
[11], one of which is domain adaptation. Therefore, domain adaptation can be treated as a special case of transfer learning. To
introduce domain adaptation and its relation to other paradigms
118

of knowledge transfer, a brief outline of various knowledge transfer paradigms is provided. These are multitask learning (MTL),
self-taught learning, sample selection bias, lifelong machine
learning (LML), zero-shot learning, and domain adaptation.
For the purpose of this discussion, the definitions of domain
and task are outlined in line with [1]. A domain D is said to consist of two components, a feature space X and a marginal probability distribution P (X ) that governs the feature space, where
X = {x 1, f, x n} 1 X is the set of samples from the feature space.
For example, if the learning task is audio transcription, the data
from different subjects can be treated as different domains. The
voice of the subject can be considered to be the feature space X,
and X = {x 1, f, x n} is the set of audio signals (words) uttered by
the subject, where P (X ) is the marginal probability that governs
X 1 X. Two domains are considered different if their feature
spaces are different (for example, different users) or their probability distributions are different (for example, casual conversation
versus reading a report). If D = {X, P (X )} is a domain, then a
task T consists of two components, T = {Y, f ($)}, where Y is
the label space and f ($) is the function f : X " Y. The function
f ($) is unknown, and, in a supervised setting, it is learned from
training data pairs (x i, y i), where x i ! X and y i ! Y. The function f (x) can then be used to predict the label of a test instance
x. From a probabilistic perspective, f (x) can be viewed as the
posterior probability p (y | x) .

MTL
In this setting, labeled training data are available for a set of
K tasks T = {T1, T2, f, TK }, where each task is associated with a different domain, D = {D 1, D 2, f, D K }. Given
the kth task, it is not possible to estimate the empirical joint
distribution Ptk (X, Y ) reliably with data from the kth domain,
D k = {x ik, y ik} ni =k 1, x ik ! X k and y ik ! Yk . A good approximation for Ptk (X, Y ) is learned by exploiting the training data from
all of the domains D = {D 1, D 2, f, D K } and learning all of
the tasks simultaneously [10]. The tasks are different irrespective
of the equality of the domains. In terms of availability of labels,
all of the domains usually have labels. Even by this definition,
Pt k (X, Y ) is inferred by combining the data from all of the tasks
and learning all of the tasks simultaneously. Transfer of knowledge between tasks enhances the performance of each individual
task. An introduction and a survey of MTL procedures is provided in [12] and [13].

Self-taught learning
The concept of self-taught learning is based on how humans
learn in an unsupervised manner from unlabeled data [14]. In this
paradigm, the transfer of knowledge is from unrelated domains
in the form of learned representations. Given unlabeled data,
{x 1u, f, x ku}, where x iu ! R d, the self-taught learning framework
estimates a set of K basis vectors that are later used as a basis to
represent the target data. Specifically,
K

min / x iu - / a i b j

	

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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
Signal Processing - November 2017 - 9
Signal Processing - November 2017 - 10
Signal Processing - November 2017 - 11
Signal Processing - November 2017 - 12
Signal Processing - November 2017 - 13
Signal Processing - November 2017 - 14
Signal Processing - November 2017 - 15
Signal Processing - November 2017 - 16
Signal Processing - November 2017 - 17
Signal Processing - November 2017 - 18
Signal Processing - November 2017 - 19
Signal Processing - November 2017 - 20
Signal Processing - November 2017 - 21
Signal Processing - November 2017 - 22
Signal Processing - November 2017 - 23
Signal Processing - November 2017 - 24
Signal Processing - November 2017 - 25
Signal Processing - November 2017 - 26
Signal Processing - November 2017 - 27
Signal Processing - November 2017 - 28
Signal Processing - November 2017 - 29
Signal Processing - November 2017 - 30
Signal Processing - November 2017 - 31
Signal Processing - November 2017 - 32
Signal Processing - November 2017 - 33
Signal Processing - November 2017 - 34
Signal Processing - November 2017 - 35
Signal Processing - November 2017 - 36
Signal Processing - November 2017 - 37
Signal Processing - November 2017 - 38
Signal Processing - November 2017 - 39
Signal Processing - November 2017 - 40
Signal Processing - November 2017 - 41
Signal Processing - November 2017 - 42
Signal Processing - November 2017 - 43
Signal Processing - November 2017 - 44
Signal Processing - November 2017 - 45
Signal Processing - November 2017 - 46
Signal Processing - November 2017 - 47
Signal Processing - November 2017 - 48
Signal Processing - November 2017 - 49
Signal Processing - November 2017 - 50
Signal Processing - November 2017 - 51
Signal Processing - November 2017 - 52
Signal Processing - November 2017 - 53
Signal Processing - November 2017 - 54
Signal Processing - November 2017 - 55
Signal Processing - November 2017 - 56
Signal Processing - November 2017 - 57
Signal Processing - November 2017 - 58
Signal Processing - November 2017 - 59
Signal Processing - November 2017 - 60
Signal Processing - November 2017 - 61
Signal Processing - November 2017 - 62
Signal Processing - November 2017 - 63
Signal Processing - November 2017 - 64
Signal Processing - November 2017 - 65
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Signal Processing - November 2017 - 67
Signal Processing - November 2017 - 68
Signal Processing - November 2017 - 69
Signal Processing - November 2017 - 70
Signal Processing - November 2017 - 71
Signal Processing - November 2017 - 72
Signal Processing - November 2017 - 73
Signal Processing - November 2017 - 74
Signal Processing - November 2017 - 75
Signal Processing - November 2017 - 76
Signal Processing - November 2017 - 77
Signal Processing - November 2017 - 78
Signal Processing - November 2017 - 79
Signal Processing - November 2017 - 80
Signal Processing - November 2017 - 81
Signal Processing - November 2017 - 82
Signal Processing - November 2017 - 83
Signal Processing - November 2017 - 84
Signal Processing - November 2017 - 85
Signal Processing - November 2017 - 86
Signal Processing - November 2017 - 87
Signal Processing - November 2017 - 88
Signal Processing - November 2017 - 89
Signal Processing - November 2017 - 90
Signal Processing - November 2017 - 91
Signal Processing - November 2017 - 92
Signal Processing - November 2017 - 93
Signal Processing - November 2017 - 94
Signal Processing - November 2017 - 95
Signal Processing - November 2017 - 96
Signal Processing - November 2017 - 97
Signal Processing - November 2017 - 98
Signal Processing - November 2017 - 99
Signal Processing - November 2017 - 100
Signal Processing - November 2017 - 101
Signal Processing - November 2017 - 102
Signal Processing - November 2017 - 103
Signal Processing - November 2017 - 104
Signal Processing - November 2017 - 105
Signal Processing - November 2017 - 106
Signal Processing - November 2017 - 107
Signal Processing - November 2017 - 108
Signal Processing - November 2017 - 109
Signal Processing - November 2017 - 110
Signal Processing - November 2017 - 111
Signal Processing - November 2017 - 112
Signal Processing - November 2017 - 113
Signal Processing - November 2017 - 114
Signal Processing - November 2017 - 115
Signal Processing - November 2017 - 116
Signal Processing - November 2017 - 117
Signal Processing - November 2017 - 118
Signal Processing - November 2017 - 119
Signal Processing - November 2017 - 120
Signal Processing - November 2017 - 121
Signal Processing - November 2017 - 122
Signal Processing - November 2017 - 123
Signal Processing - November 2017 - 124
Signal Processing - November 2017 - 125
Signal Processing - November 2017 - 126
Signal Processing - November 2017 - 127
Signal Processing - November 2017 - 128
Signal Processing - November 2017 - 129
Signal Processing - November 2017 - 130
Signal Processing - November 2017 - 131
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Signal Processing - November 2017 - 133
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Signal Processing - November 2017 - 137
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Signal Processing - November 2017 - 140
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Signal Processing - November 2017 - 174
Signal Processing - November 2017 - 175
Signal Processing - November 2017 - 176
Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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