A review of single-source unsupervised domain adaptation <s> Iterative self-labeling <s> Abstract The construction of a suitable rule of allocation in the two-population discrimination problem is considered in the case where there are initially available from the populations II1, II2, n 1, n 2 observations and M unclassified observations. An iterative reclassification procedure based on the n 1 + n 3 + M observations is proposed and found asymptotically optimal when M → ∞ and n 1 and n 2 are moderately large. The case of finite M is evaluated by a Monte Carlo experiment which suggests that the proposed procedure, after only one iteration, gives a rule with smaller average risk than the usual rule based on just the n 1 + n 2 classified observations. <s> BIB001 </s> A review of single-source unsupervised domain adaptation <s> Iterative self-labeling <s> Standard machine learning techniques assume that the statistical structure of the training and test datasets are the same (i.e. same attribute distribution p(x), and same class distribution p(c|x)). However, in real prediction problems this is not usually the case for different reasons. For example, the training set is not usually representative of the whole problem due to sample selection biases during its acquisition. In addition, the measurement biases in training could be different than in test (for example, when the measurement devices are different). Another reason is that in real prediction tasks the statistical structure of the classes is not usually static but evolves in time, and there is usually a time lag between training and test sets. Due to these different problems, the performance of a learning algorithm can severely degrade. Here we present a new learning strategy that constructs a classifier in two steps. First, the labeled examples of the training set are used for constructing a statistical model of the problem. In the second step, the model is improved using the unlabeled patterns of the test set by means of a novel extension of the Expectation-Maximization (EM) algorithm presented here. We show the convergence properties of the algorithm and illustrate its performance with an artificial problem. Finally we demonstrate its strengths in a heart disease diagnosis problem where the training set is taken from a different hospital than the test set. <s> BIB002 </s> A review of single-source unsupervised domain adaptation <s> Iterative self-labeling <s> This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy. <s> BIB003 </s> A review of single-source unsupervised domain adaptation <s> Iterative self-labeling <s> Domain adaptation algorithms seek to generalize a model trained in a source domain to a new target domain. In many practical cases, the source and target distributions can differ substantially, and in some cases crucial target features may not have support in the source domain. In this paper we introduce an algorithm that bridges the gap between source and target domains by slowly adding to the training set both the target features and instances in which the current algorithm is the most confident. Our algorithm is a variant of co-training [7], and we name it CODA (Co-training for domain adaptation). Unlike the original co-training work, we do not assume a particular feature split. Instead, for each iteration of co-training, we formulate a single optimization problem which simultaneously learns a target predictor, a split of the feature space into views, and a subset of source and target features to include in the predictor. CODA significantly out-performs the state-of-the-art on the 12-domain benchmark data set of Blitzer et al. [4]. Indeed, over a wide range (65 of 84 comparisons) of target supervision CODA achieves the best performance. <s> BIB004 </s> A review of single-source unsupervised domain adaptation <s> Iterative self-labeling <s> A strong assumption to derive generalization guarantees in the standard PAC framework is that training (or source) data and test (or target) data are drawn according to the same distribution. Because of the presence of possibly outdated data in the training set, or the use of biased collections, this assumption is often violated in real-world applications leading to different source and target distributions. To go around this problem, a new research area known as Domain Adaptation (DA) has recently been introduced giving rise to many adaptation algorithms and theoretical results in the form of generalization bounds. This paper deals with self-labeling DA whose goal is to iteratively incorporate semi-labeled target data in the learning set to progressively adapt the classifier from the source to the target domain. The contribution of this work is three-fold: First, we provide the minimum and necessary theoretical conditions for a self-labeling DA algorithm to perform an actual domain adaptation. Second, fo... <s> BIB005 </s> A review of single-source unsupervised domain adaptation <s> Iterative self-labeling <s> We provide a first PAC-Bayesian analysis for domain adaptation (DA) which arises when the learning and test distributions differ. It relies on a novel distribution pseudodistance based on a disagreement averaging. Using this measure, we derive a PAC-Bayesian DA bound for the stochastic Gibbs classifier. This bound has the advantage of being directly optimizable for any hypothesis space. We specialize it to linear classifiers, and design a learning algorithm which shows interesting results on a synthetic problem and on a popular sentiment annotation task. This opens the door to tackling DA tasks by making use of all the PAC-Bayesian tools. <s> BIB006 </s> A review of single-source unsupervised domain adaptation <s> Iterative self-labeling <s> Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution \underline{A}daptation~(BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation~(W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods. <s> BIB007 </s> A review of single-source unsupervised domain adaptation <s> Iterative self-labeling <s> Domain adaptation (DA) is the task of classifying an unlabeled dataset (target) using a labeled dataset (source) from a related domain. The majority of successful DA methods try to directly match the distributions of the source and target data by transforming the feature space. Despite their success, state of the art methods based on this approach are either involved or unable to directly scale to data with many features. This article shows that domain adaptation can be successfully performed by using a very simple randomized expectation maximization (EM) method. We consider two instances of the method, which involve logistic regression and support vector machine, respectively. The underlying assumption of the proposed method is the existence of a good single linear classifier for both source and target domain. The potential limitations of this assumption are alleviated by the flexibility of the method, which can directly incorporate deep features extracted from a pre-trained deep neural network. The resulting algorithm is strikingly easy to implement and apply. We test its performance on 36 real-life adaptation tasks over text and image data with diverse characteristics. The method achieves state-of-the-art results, competitive with those of involved end-to-end deep transfer-learning methods. <s> BIB008
A prominent approach in semi-supervised learning is to construct a classifier based on the labeled data and make predictions some of the unlabeled data. These predictions are then treated as labeled data in a following iteration and used for re-training the classifier. This is known as iterative self-labeling or self-learning BIB001 . A similar procedure can be set up for domain adaptation: train a classifier on the source data, predict the labels of the some of the target samples, and use these labeled target samples to re-train the classifier BIB002 , BIB003 . But which target samples to use first? Co-training is an approach that uses a logistic regressor to soft-label target samples BIB004 . The ones with a posterior probability larger than some threshold, are self-labeled and used in the next iteration of training the classifier. The threshold on the posterior will drop in each iteration, ensuring that the classifier becomes stable. Another method, called DASVM BIB003 , will progressively find 2k source samples and replace them by 2k newly labeled target samples. The k samples closest to the margins spanned by the current support vectors are selected. DASVM has two cost factors that trade-off the source error and the target error. By increasing the cost factor for the target error while decreasing the cost for the source error over time, the classifier focuses increasingly on its performance in the target domain. DASVM has to retrain each iteration, which means it has to solve multiple quadratic optimization problems. That can be computationally expensive. If the data is in the form of strings or trees, these optimization problems can be even more expensive BIB005 . The problem in this case is that the underlying kernel has to be positive semi-definite (PSD) and symmetric. By relaxing this constraint and employing similarity functions that need not be PSD or symmetric, a significant speed-up can be achieved BIB005 . If the target labels are treated as hidden variables, then inference can be done using an Expectation-Maximization procedure BIB002 ,  . Adaptation with Randomized Expectation Maximization (Ad-REM) uses the source classification model to compute the expected labeling of the target samples BIB008 . Given the expected labels, it maximizes the classification model's likelihood with respect to its parameters. It is hence an iterative self-labeling procedure. Where it differs from previous approaches, such as DASVM, is that it enforces class balance in the target samples: after each expectation step, a roughly equal amount of target samples assigned to each class is taken for the maximization step BIB008 . Class balance ensures more stable predictions. Self-labeling can be combined with a transformationbased technique. Balanced Distribution Adaptation (BDA) uses a source classifier to obtain soft labels for the target samples BIB007 . These soft labels are then used to estimate the conditional distributions in the target domain, p T (x | y). In turn, the conditionals are used to match the domains via a linear transformation. After transforming the source domain, the source classifier is re-trained and the target samples are assigned new soft labels. This procedure is reiterated until convergence. An alternative procedure is based on PAC-Bayesian weighted majority votes BIB006 ,  . First, the notion of perturbed-variation (PV) self-labeling is introduced, a measure that returns the proportion of target samples that are not within an ǫ-radius of any source sample  . PV is 0 when all target samples are close to a source sample. The matched target samples, i.e. those within range, are assigned the same label as their closest source sample. This constitutes the self-labeled set, which is later used for estimating the weighted majority vote for an ensemble of classifiers. In total, PV-minCq performs one iteration of selflabeling followed by ensemble training  . The advantage of a self-labeling approach is that it allows for validation: using the self-labeled target samples as a validation set, the current classifier's hyperparameters can be optimized BIB003 . This is also known as the reverse validation strategy BIB006 , BIB003 ,  .