"杨教授经典著作：迁移学习的利器，打造可靠、稳健的机器学习系统"

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Transfer learning is a crucial aspect of machine learning that allows systems to adapt rapidly to new situations, tasks, and environments. With Professor Yang's seminal work as a leading example in this field, transfer learning has become one of the few classic books that delves deep into this important area. Transfer learning enables machine learning systems to make use of auxiliary data and models to tackle target problems when there is limited data available in the target domain. This approach enhances the reliability and robustness of the systems, preventing them from straying too far from expected performance when faced with unforeseen changes. Professor Yang's book provides a comprehensive understanding of transfer learning, offering insights into how systems can effectively leverage existing knowledge to expedite learning in new contexts. By incorporating prior knowledge and experience, machine learning models can adapt more efficiently to novel scenarios, enhancing their overall performance and adaptability. The significance of transfer learning cannot be overstated, as it plays a pivotal role in enhancing the capabilities of machine learning systems in handling diverse and challenging tasks. By facilitating the transfer of knowledge across domains, transfer learning enables machines to learn more effectively and efficiently, ultimately leading to better decision-making and problem-solving capabilities. In conclusion, Professor Yang's timeless work on transfer learning serves as a cornerstone in the field, shedding light on the importance of adapting to new environments and tasks. By embracing transfer learning principles, machine learning systems can enhance their adaptability and performance, paving the way for more sophisticated and advanced applications in various domains.

Introduction

1.1 AI, Machine Learning and Transfer Learning

AI was a vision initiated by Alan Turing when he asked the famous question:

“Can machines think?” This question has motivated generations of researchers to

explore ways to make machines behave intelligently. Throughout recent history,

AI has experienced several ups and downs, much of which evolve around the cen-

tral question of how machines can acquire knowledge from the outside world.

Attempts to make machines think like humans have gone a long way, from

force-feeding rule-like knowledge bases to machine learning from data. Machine

learning has thus grown from an obscure discipline to a major industrial and so-

cietal force in automating decisions that range from online commerce and ad-

vertising to education and health care. Machine learning is becoming a general

enabling technology for the world due to its strong ability to endow machines

with knowledge by letting them learn and adapt through labeled and unlabeled

data. Machine learning produces prediction models from data, thus often requir-

ing well-deﬁned data as “teachers” to help tune statistical models. This ability in

making accurate predictions of future events are based on observations and un-

derstanding of the task domains. The data samples in the training examples are

often “labeled,” which means that observations and outcomes of predictions in

the training data are coupled and correlated. These examples are then used as

“teachers” by a machine learning algorithm to “train” a model that can be applied

to new data.

One can ﬁnd many illustrative examples of machine learning in the real world.

One example is in the area of face recognition in computer-based image analysis.

Suppose that we have obtained a large pool of photos taken indoors. A machine

learning system can then use these data to train a model that reports whether a

new photo corresponds to a person appearing in the pool. An application of this

model would be a gate security system for a building, where a task would be to

ascertain whether a visitor is an employee in the organization.

4 Introduction

Even though a machine learning model can be made to be of high quality, it can

also make mistakes, especially when the model is applied to different scenarios

from its training environments. For example, if a new photo is taken from an out-

door environment with different light intensities and levels of noise such as shad-

ows, sunlight from different angels and occlusion by passersby, the recognition

capability of the system may dramatically drop. This is because the model trained

by the machine learning system is applied to a “different” scenario. This drop in

performance shows that models can be outdated and needs updating when new

situations occur. It is this need to update or transfer models from one scenario to

another that lends importance to the topic of the book.

The need for transfer learning is not limited to image understanding. Another

example is understanding Twitter text messages by natural language processing

(NLP) techniques. Suppose we wish to classify Twitter messages into different

user moods such as happy or sad by its content. When one model is built using

a collection of Twitter messages and then applied to new data, the performance

drops quite dramatically as a different community of people will very likely ex-

press their opinions differently. This happens when we have teenagers in one

group and grown-ups in another.

As the previous examples demonstrate, a major challenge in practicing ma-

chine learning in many applications is that models do not work well in new task

domains. The reason why they do not work well may be due to one of several

reasons: lack of new training data due to the small data challenge, changes of cir-

cumstances and changes of tasks. For example, in a new situation, high-quality

training data may be in short supply if not often impossible to obtain for model

retraining, as in the case of medical diagnosis and medical imaging data.

Machine learning models cannot do well without sufﬁcient training data. Obtain-

ing and labeling new data often takes much effort and resources in a new appli-

cation domain, which is a major obstacle in realizing AI in the real world. Having

well-designed AI systems without the needed training data is like having a sports

car without an energy.

This discussion highlights a major roadblock in populating machine learning

to the practical world: it would be impossible to collect large quantities of data in

every domain before applying machine learning. Here we summarize some of the

reasons to develop such a transfer learning methodology:

1) Many applications only have small data: the current success of machine learn-

ing relies on the availability of a large amount of labeled data. However, high-

quality labeled data are often in short supply. Traditional machine learning

methods often cannot generalize well to new scenarios, a phenomenon known

as overﬁtting, and fail in many such cases.

2) Machine learning models need to be robust: traditional machine learning of-

ten makes an assumption that both the training and test data are drawn from

the same distribution. However, this assumption is too strong to hold in many

1.1 AI, Machine Learning and Transfer Learning 5

practical scenarios. In many cases, the distribution varies according to time

and space, and varies among situations, so we may never have access to new

training data to go with the same test distr ibution. In situations that differ from

the training data, the trained models need adaptation before they can be used.

3) Personalization and specialization are important issues : it is critical and prof-

itable to offer personalized service for every user according to individual tastes

and demands. In many real world applications, we can only collect very little

personal data from an individual user. As a result, traditional machine learn-

ing methods suffer from the cold start problems when we try to adapt a general

model to a speciﬁc situation.

4) User privacy and data security are important issues: often in our applications

we must work with other organizations by leveraging multiple data sets. Often

these data sets have different owners and cannot be revealed to each other

for privacy or security concerns. When building a model together, it would be

desirable for us to extract the “essence” of each data set and adapt them in

building a new model. For example, if we can adapt a general model at the

“edge” of a network of devices, then the data stored on the device need not to

be uploaded to enhance the general model; thus, privacy of the edge device

can be ensured.

These objectives for intelligent systems motivated the development of transfer

learning. In a nutshell, transfer learning refers to the machine learning paradigm

in which an algorithm extracts knowledge from one or more application scenar-

ios to help boost the learning performance in a target scenario. Compared to tra-

ditional machine learning, which requires large amounts of well-deﬁned training

data as the input, transfer learning can be understood as a new learning paradigm,

which the rest of the book will cover in detail. Transfer learning is also a motivation

to solve the so-called data sparsity and cold star t problems in many large-scale

and online applications (e.g., labeled user rating data in online recommendation

systems may be too few to allow these online systems to build a high-quality rec-

ommendation system).

Transfer learning can help promote AI in less-developed application areas, as

well as less technically developed geographical areas, even when not much la-

beled data is available in such areas. For example, suppose we wish to build a book

recommendation system in a new online shopping application. Suppose that the

book domain is so new that we do not have many transactions recorded in this do-

main. If we follow the supervised learning methodology in building a prediction

model in which we use the insufﬁcient training data in the new domain, we cannot

have a credible prediction model on users’ next purchase. However, with transfer

learning, one can look to a related, well-developed but different domain for help,

such as an existing movie recommendation domain. Exploiting transfer learning

techniques, one can ﬁnd the similarity and differences between the book and the

movie domains. For example, some authors also turn their books into movies, and

movies and books can attract similar user groups. Noticing these similarities can

6 Introduction

allow one to focus on adapting the new parts for the book-recommendation task,

which allows one to further exploit the underlying similarities between the data

sets. Then, book domain classiﬁcation and user preference learning models can

be adapted from those of the movie domain.

Based on the transfer learning methodologies, once we obtain a well-developed

model in one domain, we can bring this model to beneﬁt other similar domains.

Hence, having an accurate “distance” measure between any task domains is nec-

essary in developing a sound transfer learning methodology. If the distance be-

tween two domains is large, then we may not wish to apply transfer learning as

the learning might turn out to produce a negative effect. On the other hand, if two

domains are “close by,” transfer learning can be fruitfully applied.

In machine learning, the distance between domains can often be measured in

terms of the features that are used to describe the data. In image analysis, fea-

tures can be pixels or patches in an image pattern, such as the color or shape.

In NLP, features can be words or phrases. Once we know that two domains are

close to each other, we can ensure that AI models can be propagated from the

well-developed domains to less-developed domains, making the application of AI

less data dependent. And this can be a good sign for successful transfer learning

applications.

Being able to transfer knowledge from one domain to another allows machine

learning systems to extend their range of applicability beyond their original cre-

ation. This generalization ability helps make AI more accessible and more robust

in many areas where AI talents or resources such as computing power, data and

hardware might be scarce. In a way, transfer learning allows the promotion of AI

as a more inclusive technology that serves everyone.

To give an intuitive example, we can use an analogy to highlight the key insights

behind transfer learning. Consider driving in different countries in the world. In

the USA and China, for example, the driver’s seat is on the left of the car and drives

on the right side of the road. In Britain, the driver sits on the right side of the car,

and drives on the left side of the road. For a traveler who is used to driving in the

USA to travel to drive in Britain, it is particularly hard to switch. Transfer learning,

however, tells us to ﬁnd the invariant in the two driving domains that is a common

feature. On a closer observation, one can ﬁnd that no matter where one drives, the

driver’s distance to the center of the road is the closest. Or, conversely, the driver

sits farthest from the side of the road. This fact allows human drivers to smoothly

“transfer” from one country to another. Thus, the insight behind transfer learning

is to ﬁnd the “invariant” between domains and tasks.

Transfer learning has been studied under different terminologies in AI, such as

knowledge reuse and CBR, learning by analogy, domain adaptation, pre-training,

ﬁne-tuning, and so on. In the ﬁelds of education and learning psychology, trans-

fer of learning has a similar notion as transfer learning in machine learning. In

particular, transfer of learning refers to the process in which past experience ac-

quired from previous source tasks can be used to inﬂuence future learning and

1.2 Transfer Learning: A Deﬁnition 7

performance in a target situation (Thorndike and S. Woodworth, 1901). Trans-

fer of learning in the ﬁeld of education shares a common goal as transfer learn-

ing in machine learning in that they both address the process of learning in one

context and applying the learning in another. In both areas, the learned knowl-

edge or model is taken to a future target task for use after some adaptation. When

one delves into the literature of education theory and learning psychology (Ellis,

1965; Pugh and Bergin, 2006; Schunk, 1965; Cree and Macaulay, 2000), one can

ﬁnd that, despite the fact that transfer learning in machine learning aims to en-

dow machines with the ability to adapt and transfer of learning in education tries

to study how humans adapt in education, the processes or algorithms of transfer

are similar.

A ﬁnal note on the beneﬁt of transfer learning is in simulation technology. Often

in complex tasks, such as robotics and drug design, for example, it is too expensive

to engage real world experiments. In robotics, a mobile robot or an autonomous

vehicle needs to collect sufﬁcient training data. For example, there may be many

ways in which a car is involved in a car crash but to create car crashes is far too ex-

pensive in real life. Instead, researchers often build sophisticated simulators such

that a trained model taught in the simulator environment is applied to the real

world after adaptation via transfer learning. The transfer learning step is needed

to account for many future situations that are not seen in the simulated envir-

onment and adapt the simulated prediction models, such as obstacle avoidance

models in autonomous cars, to unforeseeable future situations.

1.2 Transfer Learning: A Deﬁnition

To start with, we deﬁne what “domain,” “task” and “transfer learning” mean by

following the notations introduced by Pan and Yang (2010). A domain D consists of

two components: a feature space X and a marginal probability distribution P

where each input instance x ∈ X . In general, if two domains are different, then

they may have different feature spaces or different marginal probability distribu-

tions. Given a speciﬁc domain, D ={X ,P

}, a task T consists of two components:

a label space Y and a function f (·) (denoted by T = {Y , f (·)}). The function f (·)

is a predictive function that can be used to make predictions on unseen instances

∗

}s. From a probabilistic viewpoint, f (x) can be written as P (y|x). In classiﬁca-

tion, labels can be binary, that is, Y ={−1,+1}, or discrete values, that is, multiple

classes. In regression, labels are of continuous values.

For simplicity, we now focus on the case where there are one source domain D

and one target domain D

. The two-domain scenario is by far the most popular of

the research works in the literature. In particular, we denote by D

={(x

, y

)}

i=1

the source domain labeled data, where x

∈X

is the data instance and y

∈Y

the corresponding class label. Similarly, we denote by D

={(x

, y

)}

i=1

the target

domain labeled data, where the input x

is in X

and y

∈Y

is the corresponding

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