A review of single-source unsupervised domain adaptation <s> Motivating examples <s> The economics of information have been established by now as an integral part of economic analysis. However, surprisingly little has been written on the implications of search (and in particular, job search) for the estimation of the wage function and its ramifications in such cases as the estimation of the determinants of labor force participation, age-earning profiles, rates of return and rates of depreciation of human capital, degree of discrimination, etc. Given a wage offer distribution, the parameters of the observed wage distribution depend on the intensity of search. The lower a personâ€™s wage demands the greater the chance of his finding an acceptable job, but the lower the wage he expects to receive and the wider the dispersion of acceptable wages around their mean. On the other hand, the job seeker may opt for a more ambitious search strategy, raising his minimum wage demand and consequently increasing the risk of remaining unemployed, but also increasing the expected wage and decreasing the dispersion of available offers. Models of wage offer distribution have traditionally been based on empirical observation of observed wage distribution. This approach may involve certain biases when applied to secondary labor groups â€" married women, teenagers and the aged. This paper attempts to point out some of these biases and suggests a method for their correction. <s> BIB001 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> This article addresses the issue of specification of econometric selectivity models and suggests approaches for the correction of selectivity bias. Our approaches provide ways to specify selectivity models without the assumption of multinormal distribution. Some flexible function forms for the correction of selectivity bias in the regression equation are derived. All the models considered can be estimated by simple consistent two stage methods. Our approaches provide simple procedures for the testing of selectivity bias without imposing restrictive distributional assumptions and also tests for the normality assumption. <s> BIB002 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> This paper surveys the available methods for estimating models with sample selection bias. I initially examine the fully parameterized model proposed by Heckman (1979) before investigating departures in two directions. First, I consider the relaxation of distributional assumptions. In doing so I present the available semi-parametric procedures. Second, I investigate the ability to tackle different selection rules generating the selection bias. Finally, I discuss how the estimation procedures applied in the cross-sectional case can be extended to panel data. <s> BIB003 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Although the selection of patients is known to be a powerful factor affecting the results of clinical trials, little is known about recruitment issues. Many patients with schizophrenia who are screened for a clinical trial of an investigational antipsychotic are ultimately not included in the study. Therefore, the question arises of whether the results obtained by studying a selected group of patients are really representative of the general population of patients with schizophrenia. The authors studied possible reasons for selective sampling in 200 patients who were consecutively admitted to inpatient units of Innsbruck's Department of Psychiatry with a diagnosis of schizophreniform or schizophrenic disorder over a time period of 33 months. Apart from demographic data and a psychopathologic rating (using the Brief Psychiatric Rating Scale), the authors recorded whether or not a patient was included in a phase III study and whether or not those were not included would have theoretically been eligible for such a study. Twenty-seven patients were finally recruited for a clinical trial. These patients were younger, on average, had a more recent onset of illness, and had experienced fewer psychotic episodes in the past. A history of noncompliance with previous treatment and the refusal of consent were the most common reasons for not including theoretically eligible patients in a clinical trial. <s> BIB004 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we seek to adapt existing models from a resource-rich source domain to a resource-poor target domain. We introduce structural correspondence learning to automatically induce correspondences among features from different domains. We test our technique on part of speech tagging and show performance gains for varying amounts of source and target training data, as well as improvements in target domain parsing accuracy using our improved tagger. <s> BIB005 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains. <s> BIB006 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Large, multi-site studies utilizing MRI-derived measures from multiple scanners present an opportunity to advance research by pooling data. On the other hand, it remains unclear whether or not the potential confound introduced by different scanners and upgrades will devalue the integrity of any results. Although there are studies of scanner differences for the purpose of calibration and quality control, the current literature is devoid of studies that describe the analysis of multi-scanner data with regard to the interaction of scanner(s) with effects of interest. We investigated a data-set of 136 subjects, 62 patients with mild to moderate Alzheimer's disease and 74 cognitively normal elderly controls, with MRI scans from one center that were acquired over 10 years with 6 different scanners and multiple upgrades over time. We used a whole-brain voxel-wise analysis to evaluate the effect of scanner, effect of disease, and the interaction of scanner and disease for the 6 different scanners. The effect of disease in patients showed the expected significant reduction of grey matter in the medial temporal lobe. Scanner differences were substantially less than the group differences and only significant in the thalamus. There was no significant interaction of scanner with disease group. We describe the rationale for concluding that our results were not confounded by scanner differences. Similar analyses in other multi-scanner data-sets could be used to justify the pooling of data when needed, such as in studies of rare disorders or in multi-center designs. <s> BIB007 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> A new method for multitask learning in a Bayesian network context is presented for multiorganism gene network estimation. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from actual edges in the true underlying Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes (human and yeast). <s> BIB008 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> We study the problem of domain transfer for a supervised classification task in mRNA splicing. We consider a number of recent domain transfer methods from machine learning, including some that are novel, and evaluate them on genomic sequence data from model organisms of varying evolutionary distance. We find that in cases where the organisms are not closely related, the use of domain adaptation methods can help improve classification performance. <s> BIB009 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> A problem in performing activity recognition on a large scale (i.e. in many homes) is that a labelled data set needs to be recorded for each house activity recognition is performed in. This is because most models for activity recognition require labelled data to learn their parameters. In this paper we introduce a transfer learning method for activity recognition which allows the use of existing labelled data sets of various homes to learn the parameters of a model applied in a new home. We evaluate our method using three large real world data sets and show our approach achieves good classification performance in a home for which little or no labelled data is available. <s> BIB010 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> MHC class I molecules are key players in the human immune system. They bind small peptides derived from intracellular proteins and present them on the cell surface for surveillance by the immune system. Prediction of such MHC class I binding peptides is a vital step in the design of peptide-based vaccines and therefore one of the major problems in computational immunology. Thousands of different types of MHC class I molecules exist, each displaying a distinct binding specificity. The lack of sufficient training data for the majority of these molecules hinders the application of Machine Learning to this problem. ::: ::: We propose two approaches to improve the predictive power of kernel-based Machine Learning methods for MHC class I binding prediction: First, a modification of the Weighted Degree string kernel that allows for the incorporation of amino acid properties. Second, we propose an enhanced Multitask kernel and an optimization procedure to fine-tune the kernel parameters. The combination of both approaches yields improved performance, which we demonstrate on the IEDB benchmark data set. <s> BIB011 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> BackgroundPrediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as GO, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the GO terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.ResultsIn this paper, we propose a Gene Ontology Based Transfer Learning Model (GO-TLM) for large-scale protein subcellular localization. The model transfers the signature-based homologous GO terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false GO terms that are resulted from evolutionary divergence. We derive three GO kernels from the three aspects of gene ontology to measure the GO similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate GO-TLM performance against three baseline models: MultiLoc, MultiLoc-GO and Euk-mPLoc on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that GO-TLM achieves substantial accuracy improvement against the baseline models: 80.38% against model Euk-mPLoc 67.40% with 12.98% substantial increase; 96.65% and 96.27% against model MultiLoc-GO 89.60% and 89.60%, with 7.05% and 6.67% accuracy increase on dataset MultiLoc plant and dataset MultiLoc animal, respectively; 97.14%, 95.90% and 96.85% against model MultiLoc-GO 83.70%, 90.10% and 85.70%, with accuracy increase 13.44%, 5.8% and 11.15% on dataset BaCelLoc plant, dataset BaCelLoc fungi and dataset BaCelLoc animal respectively. For BaCelLoc independent sets, GO-TLM achieves 81.25%, 80.45% and 79.46% on dataset BaCelLoc plant holdout, dataset BaCelLoc plant holdout and dataset BaCelLoc animal holdout, respectively, as compared against baseline model MultiLoc-GO 76%, 60.00% and 73.00%, with accuracy increase 5.25%, 20.45% and 6.46%, respectively.ConclusionsSince direct homology-based GO term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, GO-TLM) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based GO term transfer and explicitly weighing the GO kernels substantially improve the prediction performance. <s> BIB012 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> While managers’ accounting and financial decisions are, for many, fascinating topics, selection bias poses a serious challenge to researchers estimating the decisions’ effects using non-experimental data. Selection bias potentially occurs because managers’ decisions are non-random and the outcomes of choices not made are never observable. “Selection bias due to observables” arises from sample differences that researchers can observe but fail to control. “Selection bias due to unobservables” arises from the unobservable and thus uncontrolled sample differences that affect managers’ decisions and their consequences. In this article I review two econometric tools developed to mitigate these biases – the propensity score matching (PSM) method to mitigate selection bias due to observables and the Heckman inverse-Mills-ratio (IMR) method to address selection bias due to unobservables – and discuss their applications in accounting and finance research. The article has four takeaways. First, researchers should select the correct method to alleviate potential selection bias: the PSM method mitigates selection bias due to observables, but does not alleviate selection bias due to unobservables. Second, in applying PSM researchers are advised to restrict their inferences to firms whose characteristics can be found in both the sample and control groups. Third, the IMR method, though popular, is limited to situations in which the choices are binary, the outcomes of choices are modeled in a linear regression, and the unobservables in the choice and outcome models follow a multivariate normal distribution. Researchers can overcome these constraints by using full information maximum likelihood estimation. Last, when the IMR method is used, special attention should be paid to the formulas in calculating IMRs. The article also calls for researchers’ attention to other approaches to evaluating the effects of managers’ decisions. <s> BIB013 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Datasets are an integral part of contemporary object recognition research. They have been the chief reason for the considerable progress in the field, not just as source of large amounts of training data, but also as means of measuring and comparing performance of competing algorithms. At the same time, datasets have often been blamed for narrowing the focus of object recognition research, reducing it to a single benchmark performance number. Indeed, some datasets, that started out as data capture efforts aimed at representing the visual world, have become closed worlds unto themselves (e.g. the Corel world, the Caltech-101 world, the PASCAL VOC world). With the focus on beating the latest benchmark numbers on the latest dataset, have we perhaps lost sight of the original purpose? The goal of this paper is to take stock of the current state of recognition datasets. We present a comparison study using a set of popular datasets, evaluated based on a number of criteria including: relative data bias, cross-dataset generalization, effects of closed-world assumption, and sample value. The experimental results, some rather surprising, suggest directions that can improve dataset collection as well as algorithm evaluation protocols. But more broadly, the hope is to stimulate discussion in the community regarding this very important, but largely neglected issue. <s> BIB014 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> This paper focuses on performing Sentiment Analysis of Twitter by adapting data from other domains, commonly referred to as Domain Adaptation. While we show that Domain Adaptation is useful in predicting sentiments, we propose different techniques to select an out-of-domain data source that would aid in Sentiment Analysis. Additionally, we suggest two iterative algorithms based on Expectation-Maximization (EM) and Rocchio SVM that filter noisy data during adaptation and train only on valid data. Finally, we explore a couple of metrics, Mutual Information and Cosine distance to measure similarity between different domains of data. We use Twitter and Blippr as data sources and perform binary sentiment (positive and negative sentiments) classification. <s> BIB015 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Gene-expression data are now widely available and used for a wide range of clinical and diagnostic purposes. A key challenge is to select a few significant marker genes for biological studies. While it is feasible to find important genes from a single gene-expression data set, it is often more meaningful to compare the results from different but related data sets together, especially for multiple gene-expression data sets arising from different studies of a common organism or phenotype. In this paper, we present a novel framework to exploit the commonalities across different data sets by jointly learning from different data sets simultaneously through multi-task feature learning. By identifying a common subspace of genes, we can help biologists find important marker genes that span different evolutionary periods in the life cycle of cancer development. The genes thus found are more stable and more significant. Our experimental results demonstrate that more accurate models can be built using multiple data sets based on fewer labelled examples. To the best of our knowledge, we are among the first to introduce multi-task learning in the bioinformatics community to solve the lack of data problem. <s> BIB016 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Machine learning and data mining have found many applications in biological domains, where we look to build predictive models based on labeled training data. However, in practice, high quality labeled data is scarce, and to label new data incurs high costs. Transfer and multitask learning offer an attractive alternative, by allowing useful knowledge to be extracted and transferred from data in auxiliary domains helps counter the lack of data problem in the target domain. In this article, we survey recent advances in transfer and multitask learning for bioinformatics applications. In particular, we survey several key bioinformatics application areas, including sequence classification, gene expression data analysis, biological network reconstruction and biomedical applications. Category: Convergence computing <s> BIB017 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies. <s> BIB018 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Human activity recognition from accelerometer data (e.g., obtained by smart phones) is gathering a great deal of attention since it can be used for various purposes such as remote health-care. However, since collecting labeled data is bothersome for new users, it is desirable to utilize data obtained from existing users. In this paper, we formulate this adaptation problem as learning under covariate shift, and propose a computationally efficient probabilistic classification method based on adaptive importance sampling. The usefulness of the proposed method is demonstrated in real-world human activity recognition. <s> BIB019 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> There has been little work on examining how deep neural networks may be adapted to speakers for improved speech recognition accuracy. Past work has examined using a discriminatively trained affine transformation of the input features applied at a frame level or the re-training of the entire shallow network for a specific speaker. This work explores how deep neural networks may be adapted to speakers by re-training the input layer, the output layer or the entire network. We look at how L2 regularization using weight decay to the speaker independent model improves generalization. Other training factors are examined including the role momentum plays and stochastic mini-batch versus batch training. While improvements are significant for smaller networks, the largest show little gain from adaptation on a large vocabulary mobile speech recognition task. <s> BIB020 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> The way in which human beings express emotions depends on their specific personality and cultural background. As a consequence, person independent facial expression classifiers usually fail to accurately recognize emotions which vary between different individuals. On the other hand, training a person-specific classifier for each new user is a time consuming activity which involves collecting hundreds of labeled samples. In this paper we present a personalization approach in which only unlabeled target-specific data are required. The method is based on our previous paper [20] in which a regression framework is proposed to learn the relation between the user's specific sample distribution and the parameters of her/his classifier. Once this relation is learned, a target classifier can be constructed using only the new user's sample distribution to transfer the personalized parameters. The novelty of this paper with respect to [20] is the introduction of a new method to represent the source sample distribution based on using only the Support Vectors of the source classifiers. Moreover, we present here a simplified regression framework which achieves the same or even slightly superior experimental results with respect to [20] but it is much easier to reproduce. <s> BIB021 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> With the availability of speech data obtained from different devices and varied acquisition conditions, we are often faced with scenarios, where the intrinsic discrepancy between the training and the test data has an adverse impact on affective speech analysis. To address this issue, this letter introduces an Adaptive Denoising Autoencoder based on an unsupervised domain adaptation method, where prior knowledge learned from a target set is used to regularize the training on a source set. Our goal is to achieve a matched feature space representation for the target and source sets while ensuring target domain knowledge transfer. The method has been successfully evaluated on the 2009 INTERSPEECH Emotion Challenge’s FAU Aibo Emotion Corpus as target corpus and two other publicly available speech emotion corpora as sources. The experimental results show that our method significantly improves over the baseline performance and outperforms related feature domain adaptation methods. <s> BIB022 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%. <s> BIB023 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Abstract In astronomical applications of machine learning, the distribution of objects used for building a model is often different from the distribution of the objects the model is later applied to. This is known as sample selection bias, which is a major challenge for statistical inference as one can no longer assume that the labeled training data are representative. To address this issue, one can re-weight the labeled training patterns to match the distribution of unlabeled data that are available already in the training phase. There are many examples in practice where this strategy yielded good results, but estimating the weights reliably from a finite sample is challenging. We consider an efficient nearest neighbor density ratio estimator that can exploit large samples to increase the accuracy of the weight estimates. To solve the problem of choosing the right neighborhood size, we propose to use cross-validation on a model selection criterion that is unbiased under covariate shift. The resulting algorithm is our method of choice for density ratio estimation when the feature space dimensionality is small and sample sizes are large. The approach is simple and, because of the model selection, robust. We empirically find that it is on a par with established kernel-based methods on relatively small regression benchmark datasets. However, when applied to large-scale photometric redshift estimation, our approach outperforms the state-of-the-art. <s> BIB024 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation. <s> BIB025 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> We investigate the task of detecting speakers in crowded environments using a single triaxial accelerometer worn around the neck. Similar to the previous studies, by assuming that body movements are indicative of speech, we show experimentally that transductive transfer learning can better model individual differences in speaking behaviour compared to a traditional person independent setup. Such behaviour is very challenging to model as people's body movements during speech vary greatly. To our knowledge, this is the first time that a transfer learning approach has been considered in the context of speaking status detection using a single body worn accelerometer. We show that by transferring knowledge across subjects, competitive performance scores compared to a person dependent training can be obtained. <s> BIB026 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. Our method consists of both global and category specific adaptation techniques. Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. This initially adapted space then enables category specific adaptation through a generalization of constrained weak learning, with explicit transfer of the spatial layout from the source to the target domains. Our approach outperforms baselines across different settings on multiple large-scale datasets, including adapting across various real city environments, different synthetic sub-domains, from simulated to real environments, and on a novel large-scale dash-cam dataset. <s> BIB027 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, (1) How much data from the new domain is required for a decent adaptation of the original network?; and, (2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset. The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch. <s> BIB028 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> We investigate the task of detecting speakers in crowded environments using a single body worn triaxial accelerometer. Detection of such behaviour is very challenging to model as people's body movements during speech vary greatly. Similar to previous studies, by assuming that body movements are indicative of speech, we show experimentally, on a real-world dataset of 3 h including 18 people, that transductive parameter transfer learning (Zen et al. in Proceedings of the 16th international conference on multimodal interaction. ACM, 2014) can better model individual differences in speaking behaviour, significantly improving on the state-of-the-art performance. We also discuss the challenges introduced by the in-the-wild nature of our dataset and experimentally show how they affect detection performance. We strengthen the need for an adaptive approach by comparing the speech detection problem to a more traditional activity (i.e. walking). We provide an analysis of the transfer by considering different source sets which provides a deeper investigation of the nature of both speech and body movements, in the context of transfer learning. <s> BIB029 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While supervised learning optimises a model for the training domain, it will deliver degraded performance in application domains that underlie distributional shifts caused by these changes. Traditionally, this problem has been addressed via the collection of labelled data in multiple domains or by imposing priors on the type of shift between both domains. We frame the problem in the context of unsupervised domain adaptation and develop a framework for applying adversarial techniques to adapt popular, state-of-the-art network architectures with the additional objective to align features across domains. Moreover, as adversarial training is notoriously unstable, we first perform an extensive ablation study, adapting many techniques known to stabilise generative adversarial networks, and evaluate on a surrogate classification task with the same appearance change. The distilled insights are applied to the problem of free-space segmentation for motion planning in autonomous driving. <s> BIB030 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. Experiments show that our proposed virtual to real (VR) reinforcement learning (RL) works pretty well. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data. <s> BIB031 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we propose a Siamese neural network (MRAI-NET) that extracts acquisition-invariant feature vectors. These can consequently be used by task-specific methods, such as voxelwise classifiers for tissue segmentation. MRAI-NET is evaluated on both simulated and real patient data. Experiments show that MRAI-NET outperforms both voxelwise classifiers trained on the source data as well as classifiers trained on the limited amount of target scanner data available. <s> BIB032 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images. We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN. We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50 times, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples. <s> BIB033 </s> A review of single-source unsupervised domain adaptation <s> Motivating examples <s> The i-vector approach to speaker recognition has achieved good performance when the domain of the evaluation dataset is similar to that of the training dataset. However, in realworld applications, there is always a mismatch between the training and evaluation datasets, that leads to performance degradation. To address this problem, this paper proposes to learn the domain-invariant and speaker-discriminative speech representations via domain adversarial training. Specifically, with domain adversarial training method, we use a gradient reversal layer to remove the domain variation and project the different domain data into the same subspace. Moreover, we compare the proposed method with other state-of-the-art unsupervised domain adaptation techniques for i-vector approach to speaker recognition (e.g. autoencoder based domain adaptation, inter dataset variability compensation, dataset-invariant covariance normalization, and so on). Experiments on 2013 domain adaptation challenge (DAC) dataset demonstrate that the proposed method is not only effective in solving the dataset mismatch problem, but also outperforms the compared unsupervised domain adaptation methods. <s> BIB034
One of the common causes of a domain adaptation problem is sample selection bias. Selection biases have been studied in statistics and econometrics for a long time BIB002 ,  , BIB013 . In the 70s, it was of interest to find predictors for wage rates of women. These were estimated by measuring characteristics of working women and their salaries BIB001 , BIB003 . However, working women differed from non-working women on characteristics such as age, number of children, and education BIB001 . Therefore, the predictor did not generalize to the total population. In clinical studies, to study the effect of a treatment, patients are assigned to an experimental group versus a control group  ,  . For example, the experimental group will receive a new antipsychotic drug and the control group a placebo BIB004 . The effect of the treatment is measured by comparing the average difference between the groups on some metric of health. The treatment effect is expected to hold for other patients as well, but it will not necessarily hold in patients that were systematically excluded from the study. For example, the factor that is making patients non-compliant, a reason for exclusion, could also have an effect on the treatment  , BIB004 . In order to obtain training data for computer-aideddiagnosis systems, radiologists manually annotate tissues, abnormalities, and pathologies in medical images. But due to the mechanical configuration, calibration, vendor or acquisition protocol of MRI, CT or PET scanners, there are large variations between data from different medical centers BIB007 , BIB028 . Consequently, systems trained on data from other scanners would fail to perform well on the new scanner BIB023 , BIB025 , BIB032 . Cameras come in many varieties of optics and resolutions. Each produces a slightly different image from the same object, which leads to an unwanted source of variation across image data sets BIB014 . Another example from computer vision is activity recognition in video processing: systems that recognize movements have to adapt across different surroundings and sensors BIB010 , as well as different people BIB019 , BIB026 . Similarly, general face recognition systems can be adapted to specific persons BIB021 and person-independent activity recognition algorithms can be specialized to particular individuals BIB029 . Self-driving cars face a wide variety of views along streets and roads. Domain adaptation can be very helpful to improve things like lane and pedestrian detection BIB027 , BIB030 . Furthermore, using physical simulations as source domains can significantly improve a robot's performance for real-world data, such as for hand grasping BIB033 or autonomous driving BIB031 . For humans, speech is recognizable across speakers. But learning algorithms expect speech to sound similar to their training set BIB020 , BIB022 . It is not trivial for a learning algorithm to recognize a new person. With the adoption of commercial speech recognition systems in homes, there is an increasing need to adapt to specific speakers BIB034 . Authors use different words and styles to express themselves on different publication platforms. For instance, biomedical science articles contain words like 'oncogenic' and 'mutated', which appear far less often in financial news articles BIB005 . Similarly, online movie reviews are linguistically different from tweets BIB015 and product reviews differ per category BIB006 . Natural language processing tasks such as sentiment classification become much more challenging with these word frequency statistics changes. Adaptive approaches have been successful in sequence classification BIB011 , BIB012 , gene expression analysis  , BIB016 , and biological network reconstruction BIB008 ,  . For some problem settings, the goal is to generalize from one model organism to another BIB017 , such as from nematodes (C. elegans) to fruit flies (D. melanogaster) BIB009 . Radiotelescopes measure spectral signals arriving to earth. Astronomers measure spectral signals from space to, for example, determine the amount of photometric redshift in galaxies, which is important to determining the direction in which the universe is expanding BIB024 . These signals are costly to interpret and label. Astronomers tend to pick the ones they consider the most promising. A classifier trained one these signals does not generalize well because the remaining signals are less clear. A similar data shift occurs in quasar detection BIB018 .