A survey of transfer learning <s> Transfer learning applications <s> We build word models for American Sign Language (ASL) that transfer between different signers and different aspects. This is advantageous because one could use large amounts of labelled avatar data in combination with a smaller amount of labelled human data to spot a large number of words in human data. Transfer learning is possible because we represent blocks of video with novel intermediate discriminative features based on splits of the data. By constructing the same splits in avatar and human data and clustering appropriately, our features are both discriminative and semantically similar: across signers similar features imply similar words. We demonstrate transfer learning in two scenarios: from avatar to a frontally viewed human signer and from an avatar to human signer in a 3/4 view. <s> BIB001 </s> A survey of transfer learning <s> Transfer learning applications <s> The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring useritem rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods. <s> BIB002 </s> A survey of transfer learning <s> Transfer learning applications <s> Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately. <s> BIB003 </s> A survey of transfer learning <s> Transfer learning applications <s> Link prediction is a key technique in many applications such as recommender systems, where potential links between users and items need to be predicted. A challenge in link prediction is the data sparsity problem. In this paper, we address this problem by jointly considering multiple heterogeneous link prediction tasks such as predicting links between users and different types of items including books, movies and songs, which we refer to as the collective link prediction (CLP) problem. We propose a nonparametric Bayesian framework for solving the CLP problem, which allows knowledge to be adaptively transferred across heterogeneous tasks while taking into account the similarities between tasks. We learn the inter-task similarity automatically. We also introduce link functions for different tasks to correct their biases and skewness of distributions in their link data. We conduct experiments on several real world datasets and demonstrate significant improvements over several existing state-of-the-art methods. <s> BIB004 </s> A survey of transfer learning <s> Transfer learning applications <s> Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem. <s> BIB005 </s> A survey of transfer learning <s> Transfer learning applications <s> Data sparsity due to missing ratings is a major challenge for collaborative filtering (CF) techniques in recommender systems. This is especially true for CF domains where the ratings are expressed numerically. We observe that, while we may lack the information in numerical ratings, we may have more data in the form of binary ratings. This is especially true when users can easily express themselves with their likes and dislikes for certain items. In this paper, we explore how to use the binary preference data expressed in the form of like/dislike to help reduce the impact of data sparsity of more expressive numerical ratings. We do this by transferring the rating knowledge from some auxiliary data source in binary form (that is, likes or dislikes), to a target numerical rating matrix. Our solution is to model both numerical ratings and like/dislike in a principled way, using a novel framework of Transfer by Collective Factorization (TCF). In particular, we construct the shared latent space collectively and learn the data-dependent effect separately. A major advantage of the TCF approach over previous collective matrix factorization (or bifactorization) methods is that we are able to capture the data-dependent effect when sharing the data-independent knowledge, so as to increase the over-all quality of knowledge transfer. Experimental results demonstrate the effectiveness of TCF at various sparsity levels as compared to several state-of-the-art methods. <s> BIB006 </s> A survey of transfer learning <s> Transfer learning applications <s> Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. In this paper we propose a transfer learning technique that extracts knowledge from multiple domains containing rich data (e.g., movies and music) and generates recommendations for a sparse target domain (e.g., games). Our method learns the relatedness between the different source domains and the target domain, without requiring overlapping users between domains. The model integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data. Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results. <s> BIB007 </s> A survey of transfer learning <s> Transfer learning applications <s> Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods. <s> BIB008 </s> A survey of transfer learning <s> Transfer learning applications <s> Social networks enable users to create different types of personal items. In dealing with serious information overload, the major problems of social recommendation are sparsity and cold start. In existing approaches, relational and heterogeneous domains can not be effectively utilized for social recommendation, which brings a challenge to model users and multiple types of items together on social networks. In this paper, we consider how to represent social networks with multiple relational domains and alleviate the major problems in an individual domain by transferring knowledge from other domains. We propose a novel Hybrid Random Walk (HRW), which can integrate multiple heterogeneous domains including directed/undirected links, signed/unsigned links and within-domain/cross-domain links into a star-structured hybrid graph with user graph at the center. We perform random walk until convergence and use the steady state distribution for recommendation. We conduct experiments on a real social network dataset and show that our method can significantly outperform existing social recommendation approaches. <s> BIB009 </s> A survey of transfer learning <s> Transfer learning applications <s> Recommender systems, especially the newly launched ones, have to deal with the data-sparsity issue, where little existing rating information is available. Recently, transfer learning has been proposed to address this problem by leveraging the knowledge from related recommender systems where rich collaborative data are available. However, most previous transfer learning models assume that entity-correspondences across different systems are given as input, which means that for any entity (e.g., a user or an item) in a target system, its corresponding entity in a source system is known. This assumption can hardly be satisfied in real-world scenarios where entity-correspondences across systems are usually unknown, and the cost of identifying them can be expensive. For example, it is extremely difficult to identify whether a user A from Facebook and a user B from Twitter are the same person. In this paper, we propose a framework to construct entity correspondence with limited budget by using active learning to facilitate knowledge transfer across recommender systems. Specifically, for the purpose of maximizing knowledge transfer, we first iteratively select entities in the target system based on our proposed criterion to query their correspondences in the source system. We then plug the actively constructed entity-correspondence mapping into a general transferred collaborative-filtering model to improve recommendation quality. We perform extensive experiments on real world datasets to verify the effectiveness of our proposed framework for this crosssystem recommendation problem. <s> BIB010 </s> A survey of transfer learning <s> Transfer learning applications <s> Head pose classification from surveillance images acquired with distant, large field-of-view cameras is difficult as faces are captured at low-resolution and have a blurred appearance. Domain adaptation approaches are useful for transferring knowledge from the training (source) to the test (target) data when they have different attributes, minimizing target data labeling efforts in the process. This paper examines the use of transfer learning for efficient multi-view head pose classification with minimal target training data under three challenging situations: (i) where the range of head poses in the source and target images is different, (ii) where source images capture a stationary person while target images capture a moving person whose facial appearance varies under motion due to changing perspective, scale and (iii) a combination of (i) and (ii). On the whole, the presented methods represent novel transfer learning solutions employed in the context of multi-view head pose classification. We demonstrate that the proposed solutions considerably outperform the state-of-the-art through extensive experimental validation. Finally, the DPOSE dataset compiled for benchmarking head pose classification performance with moving persons, and to aid behavioral understanding applications is presented in this work. <s> BIB011 </s> A survey of transfer learning <s> Transfer learning applications <s> This paper presents the design of a fully deployed multistage transfer learning system for targeted display advertising, highlighting the important role of problem formulation and the sampling of data from distributions different from that of the target environment. Notably, the machine learning system itself is deployed and has been in continual use for years for thousands of advertising campaigns--in contrast to the more common case where predictive models are built outside the system, curated, and then deployed. In this domain, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate distributions and learning tasks, and then transferred to the target task. We present the design of the transfer learning system We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We also present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from over half a decade of research and development on this complex, deployed, and intensely used machine learning system. <s> BIB012 </s> A survey of transfer learning <s> Transfer learning applications <s> In this paper we investigate the use of the Transfer Learning (TL) framework to extract the commonalities across a set of subjects and also to learn the way each individual instantiates these commonalities to model idiosyncrasy. To implement this we apply three variants of Multi Task Learning, namely: Regularized Multi Task Learning (RMTL), Multi Task Feature Learning (MTFL) and Composite Multi Task Feature Learning (CMTFL). Two datasets are used; the first is a set of point based facial expressions with annotated discrete levels of pain. The second consists of full body motion capture data taken from subjects diagnosed with chronic lower back pain. A synchronized electromyographic signal from the lumbar paraspinal muscles is taken as a pain-related behavioural indicator. We compare our approaches with Ridge Regression which is a comparable model without the Transfer Learning property; as well as with a subtractive method for removing idiosyncrasy. The TL based methods show statistically significant improvements in correlation coefficients between predicted model outcomes and the target values compared to baseline models. In particular RMTL consistently outperforms all other methods; a paired t-test between RMTL and the best performing baseline method returned a maximum p-value of 2.3 × 10-4. <s> BIB013 </s> A survey of transfer learning <s> Transfer learning applications <s> In speech emotion recognition, training and test data used for system development usually tend to fit each other perfectly, but further 'similar' data may be available. Transfer learning helps to exploit such similar data for training despite the inherent dissimilarities in order to boost a recogniser's performance. In this context, this paper presents a sparse auto encoder method for feature transfer learning for speech emotion recognition. In our proposed method, a common emotion-specific mapping rule is learnt from a small set of labelled data in a target domain. Then, newly reconstructed data are obtained by applying this rule on the emotion-specific data in a different domain. The experimental results evaluated on six standard databases show that our approach significantly improves the performance relative to learning each source domain independently. <s> BIB014 </s> A survey of transfer learning <s> Transfer learning applications <s> Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper, we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed. <s> BIB015 </s> A survey of transfer learning <s> Transfer learning applications <s> BACKGROUND ::: Data-driven risk stratification models built using data from a single hospital often have a paucity of training data. However, leveraging data from other hospitals can be challenging owing to institutional differences with patients and with data coding and capture. ::: ::: ::: OBJECTIVE ::: To investigate three approaches to learning hospital-specific predictions about the risk of hospital-associated infection with Clostridium difficile, and perform a comparative analysis of the value of different ways of using external data to enhance hospital-specific predictions. ::: ::: ::: MATERIALS AND METHODS ::: We evaluated each approach on 132 853 admissions from three hospitals, varying in size and location. The first approach was a single-task approach, in which only training data from the target hospital (ie, the hospital for which the model was intended) were used. The second used only data from the other two hospitals. The third approach jointly incorporated data from all hospitals while seeking a solution in the target space. ::: ::: ::: RESULTS ::: The relative performance of the three different approaches was found to be sensitive to the hospital selected as the target. However, incorporating data from all hospitals consistently had the highest performance. ::: ::: ::: DISCUSSION ::: The results characterize the challenges and opportunities that come with (1) using data or models from collections of hospitals without adapting them to the site at which the model will be used, and (2) using only local data to build models for small institutions or rare events. ::: ::: ::: CONCLUSIONS ::: We show how external data from other hospitals can be successfully and efficiently incorporated into hospital-specific models. <s> BIB016 </s> A survey of transfer learning <s> Transfer learning applications <s> Abstract To keep the advantage of Support Vector Machine (SVM) in analyzing the dynamic evolution of the dust aerosol, we introduce transfer learning as a new method because transfer learning can utilize knowledge from previously collected data and add dozens of new samples, which can significantly improve dust and cloud classification results. It can also reduce the time of sample collection and make learning efficient. In this paper, we receive significant improvement effect using SVM as the basic learner in TrAdaBoost during four consecutive dust storm days, and correct one error classification in PDF. As a result, dust aerosol in high altitude can even spread to stratosphere. Moreover, in the process of dust aerosol transportation, it is highly affected by anthropogenic aerosol, for example, the color ratio (CR) changes from 0.728 to 0.460 and finally reaches 0.466, while depolarization ratio (DR) changes from 0.308 to 0.081 and finally reaches 0.156. It is indicated that the big size and non-spherical aerosol particles reduce obviously after dust aerosol deposition, but small size and spherical anthropogenic aerosol also produce a certain effect, and on March 22, 2010 had a small recovery above the ocean following the reduction of DR and CR. Due to the MODIS resolution not meeting the observation requirement and layer identification being different between CALIPSO and CloudSat, a problem such as stratocumulus cloud in low altitude still exists in aerosol and cloud classification. Lack of ground-based auxiliary data is the main problem which hinders our validation and quantitative analysis. It is pressing for a solution in future. <s> BIB017 </s> A survey of transfer learning <s> Transfer learning applications <s> The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field. <s> BIB018 </s> A survey of transfer learning <s> Transfer learning applications <s> BackgroundMost ‘transcriptomic’ data from microarrays are generated from small sample sizes compared to the large number of measured biomarkers, making it very difficult to build accurate and generalizable disease state classification models. Integrating information from different, but related, ‘transcriptomic’ data may help build better classification models. However, most proposed methods for integrative analysis of ‘transcriptomic’ data cannot incorporate domain knowledge, which can improve model performance. To this end, we have developed a methodology that leverages transfer rule learning and functional modules, which we call TRL-FM, to capture and abstract domain knowledge in the form of classification rules to facilitate integrative modeling of multiple gene expression data. TRL-FM is an extension of the transfer rule learner (TRL) that we developed previously. The goal of this study was to test our hypothesis that “an integrative model obtained via the TRL-FM approach outperforms traditional models based on single gene expression data sources”.ResultsTo evaluate the feasibility of the TRL-FM framework, we compared the area under the ROC curve (AUC) of models developed with TRL-FM and other traditional methods, using 21 microarray datasets generated from three studies on brain cancer, prostate cancer, and lung disease, respectively. The results show that TRL-FM statistically significantly outperforms TRL as well as traditional models based on single source data. In addition, TRL-FM performed better than other integrative models driven by meta-analysis and cross-platform data merging.ConclusionsThe capability of utilizing transferred abstract knowledge derived from source data using feature mapping enables the TRL-FM framework to mimic the human process of learning and adaptation when performing related tasks. The novel TRL-FM methodology for integrative modeling for multiple ‘transcriptomic’ datasets is able to intelligently incorporate domain knowledge that traditional methods might disregard, to boost predictive power and generalization performance. In this study, TRL-FM’s abstraction of knowledge is achieved in the form of functional modules, but the overall framework is generalizable in that different approaches of acquiring abstract knowledge can be integrated into this framework. <s> BIB019 </s> A survey of transfer learning <s> Transfer learning applications <s> In pattern recognition and computer vision, one is often faced with scenarios where the training data used to learn a model have different distribution from the data on which the model is applied. Regardless of the cause, any distributional change that occurs after learning a classifier can degrade its performance at test time. Domain adaptation tries to mitigate this degradation. In this article, we provide a survey of domain adaptation methods for visual recognition. We discuss the merits and drawbacks of existing domain adaptation approaches and identify promising avenues for research in this rapidly evolving field. <s> BIB020 </s> A survey of transfer learning <s> Transfer learning applications <s> Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. <s> BIB021 </s> A survey of transfer learning <s> Transfer learning applications <s> Recommender systems can suffer from data scarcity and cold start issues. However, social networks, which enable users to build relationships and create different types of items, present an unprecedented opportunity to alleviate these issues. In this paper, we represent a social network as a star-structured hybrid graph centered on a social domain, which connects with other item domains. With this innovative representation, useful knowledge from an auxiliary domain can be transferred through the social domain to a target domain. Various factors of item transferability, including popularity and behavioral consistency, are determined. We propose a novel Hybrid Random Walk (HRW) method, which incorporates such factors, to select transferable items in auxiliary domains, bridge cross-domain knowledge with the social domain, and accurately predict user-item links in a target domain. Extensive experiments on a real social dataset demonstrate that HRW signiﬁcantly outperforms existing approaches. <s> BIB022
The surveyed works in this paper demonstrate that transfer learning has been applied to many real-world applications. There are a number of application examples pertaining to natural language processing, more specifically in the areas of sentiment classification, text classification, spam email detection, and multiple language text classification. Other well-represented transfer learning applications include image classification and video concept classification. Applications that are more selectively addressed in the previous papers include WiFi localization classification, muscle fatigue classification, drug efficacy classification, human activity classification, software defect classification, and cardiac arrhythmia classification. The majority of the solutions surveyed are generic, meaning the solution can be easily applied to applications other than the ones implemented and tested in the papers. The application-specific solutions tend to be related to the field of natural language processing and image processing. In the literature, there are a number of transfer learning solutions that are specific to the application of recommendation systems. Recommendation systems provide users with recommendations or ratings for a particular domain (e.g. movies, books, etc.), which are based on historical information. However, when the system does not have sufficient historical information (referred to as the data sparsity issue presented in Moreno BIB007 ), then the recommendations are not reliable. In the cases where the system does not have sufficient domain data to make reliable predictions (for example when a movie is just released), there is a need to use previously collected information from a different domain (using books for example). The aforementioned problem has been directly addressed using transfer learning methodologies and captured in papers by Moreno BIB007 , Cao BIB004 , Li BIB002 , Li BIB003 , Pan BIB005 , Zhang BIB008 , Pan BIB006 , Roy BIB022 , Jiang BIB009 , and Zhao BIB010 . Transfer learning solutions continue to be applied to a diverse number of real-world applications, and in some cases the applications are quite obscure. The application of head pose classification finds a learner trained with previously captured labeled head positions to predict a new head position. Head pose classification is used for determining the attentiveness of drivers, analyzing social behavior, and human interaction with robots. Head positions captured in source training data will have different head tilt ranges and angles than that of the predicted target. The paper by Rajagopal BIB011 addresses the head pose classification issues using transfer learning solutions. Other transfer learning applications include the paper by Ma BIB017 that uses transfer learning for atmospheric dust aerosol particle classification to enhance global climate models. Here the TrAdaBoost algorithm proposed by Dai [21] is used in conjunction with an SVM classifier to improve on classification results. Being able to identify areas of low income in developing countries is important for disaster relief efforts, food security, and achieving sustainable growth. To better predict poverty mapping, Xie BIB018 proposes an approach similar to Oquab [81] that uses a convolution neural network model. The first prediction model is trained to predict night time light intensity from source image data. The final target prediction model predicts the poverty mapping from source night time light intensity data. In the paper by Ogoe BIB019 , transfer learning in used to enhance disease prediction. In this solution, a rule-based learning approach is formulated to use abstract source domain data to perform modeling of multiple types of gene expression data. Online display web advertising is a growing industry where transfer learning is used to optimally predict targeted ads. In the paper by Perlich BIB012 , a transfer learning approach is employed that uses the weighted outputs of multiple source classifiers to enhance a target classifier trained to predict targeted online display advertising results. The paper by Kan [56] addresses the field of facial recognition and is able to use face image information from one ethnic group to improve the learning of a classifier for a different ethnic group. The paper by Farhadi BIB001 is focused on the application of sign language recognition where the model is able to learn from different people signing at various angles. Transfer learning is applied to the field of biology in the paper by Widmer  . Specifically, a multi-task learning approach is used in the prediction of splice sites in genome biology. Predicting if patients will contract particular bacteria when admitted to a hospital is addressed in the paper by Wiens BIB016 . Information taken from different hospitals is used to predict the infection rate for a different hospital. In the paper by Romera-Paredes BIB013 , a multi-task transfer learning approach is used to predict pain levels from an individual's facial expression by using labeled source facial images from other individuals. The paper by Deng BIB014 applies transfer learning to the application of speech emotion recognition where information is transferred from multiple labeled speech sources. The application of wine quality classification is implemented in Zhang [141] using a multi-task transfer learning approach. As a reference, the survey paper by Cook BIB015 covers transfer learning for the application of activity recognition and the survey papers by Patel BIB020 and Shao BIB021 address transfer learning in the domain of image recognition.