You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Transfer Learning for Cross-Dataset Recognition: A Survey 
Chapter Title:  Labelled Target Dataset 
1. Abstract of Cited Paper (BIB001):  Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus. 
2. Abstract of Cited Paper (BIB002):  Exploding amounts of multimedia data increasingly require automatic indexing and classification, e.g. training classifiers to produce high-level features, or semantic concepts, chosen to represent image content, like car, person, etc. When changing the applied domain (i.e. from news domain to consumer home videos), the classifiers trained in one domain often perform poorly in the other domain due to changes in feature distributions. Additionally, classifiers trained on the new domain alone may suffer from too few positive training samples. Appropriately adapting data/models from an old domain to help classify data in a new domain is an important issue. In this work, we develop a new cross-domain SVM (CDSVM) algorithm for adapting previously learned support vectors from one domain to help classification in another domain. Better precision is obtained with almost no additional computational cost. Also, we give a comprehensive summary and comparative study of the state- of-the-art SVM-based cross-domain learning methods. Evaluation over the latest large-scale TRECVID benchmark data set shows that our CDSVM method can improve mean average precision over 36 concepts by 7.5%. For further performance gain, we also propose an intuitive selection criterion to determine which cross-domain learning method to use for each concept. 
3. Abstract of Cited Paper (BIB003):  This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. 
4. Abstract of Cited Paper (BIB004):  Distance metric learning plays a very crucial role in many data mining algorithms because the performance of an algorithm relies heavily on choosing a good metric. However, the labeled data available in many applications is scarce and hence the metrics learned are often unsatisfactory. In this paper, we consider a transfer learning setting in which some related source tasks with labeled data are available to help the learning of the target task. We first propose a convex formulation for multi-task metric learning by modeling the task relationships in the form of a task covariance matrix. Then we regard transfer learning as a special case of multi-task learning and adapt the formulation of multi-task metric learning to the transfer learning setting for our method, called transfer metric learning (TML). In TML, we learn the metric and the task covariances between the source tasks and the target task under a unified convex formulation. To solve the convex optimization problem, we use an alternating method in which each subproblem has an efficient solution. Experimental results on some commonly used transfer learning applications demonstrate the effectiveness of our method. 
5. Abstract of Cited Paper (BIB005):  Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. 
6. Abstract of Cited Paper (BIB006):  Activity recognition aims to identify and predict human activities based on a series of sensor readings. In recent years, machine learning methods have become popular in solving activity recognition problems. A special difficulty for adopting machine learning methods is the workload to annotate a large number of sensor readings as training data. Labeling sensor readings for their corresponding activities is a time-consuming task. In practice, we often have a set of labeled training instances ready for an activity recognition task. If we can transfer such knowledge to a new activity recognition scenario that is different from, but related to, the source domain, it will ease our effort to perform manual labeling of training data for the new scenario. In this paper, we propose a transfer learning framework based on automatically learning a correspondence between different sets of sensors to solve this transfer-learning in activity recognition problem. We validate our framework on two different datasets and compare it against previous approaches of activity recognition, and demonstrate its effectiveness. 
7. Abstract of Cited Paper (BIB007):  Conventional human action recognition algorithms cannot work well when the amount of training videos is insufficient. We solve this problem by proposing a transfer topic model (TTM), which utilizes information extracted from videos in the auxiliary domain to assist recognition tasks in the target domain. The TTM is well characterized by two aspects: 1) it uses the bag-of-words model trained from the auxiliary domain to represent videos in the target domain; and 2) it assumes each human action is a mixture of a set of topics and uses the topics learned from the auxiliary domain to regularize the topic estimation in the target domain, wherein the regularization is the summation of Kullback-Leibler divergences between topic pairs of the two domains. The utilization of the auxiliary domain knowledge improves the generalization ability of the learned topic model. Experiments on Weizmann and KTH human action databases suggest the effectiveness of the proposed TTM for cross-domain human action recognition. 
8. Abstract of Cited Paper (BIB008):  We consider the problem of transferring some a priori knowledge in the context of supervised metric learning approaches. While this setting has been successfully applied in some empirical contexts, no theoretical evidence exists to justify this approach. In this paper, we provide a theoretical justification based on the notion of algorithmic stability adapted to the regularized metric learning setting. We propose an on-average-replace-two-stability model allowing us to prove fast generalization rates when an auxiliary source metric is used to bias the regularizer. Moreover, we prove a consistency result from which we show the interest of considering biased weighted regularized formulations and we provide a solution to estimate the associated weight. We also present some experiments illustrating the interest of the approach in standard metric learning tasks and in a transfer learning problem where few labelled data are available.

Literature Review:
In this problem, a small number of labelled data in target domain are available. However, the labelled data in target domain are generally not sufficient for classification tasks. The characteristics of this problem can be found in Table I , which corresponds to supervised domain adaptation among the literature. Feature Representations-based Transfer. The class-based criterion can be used for guiding the new representations of features for reducing the domain shift. For example, BIB001 propose a feature augmentation based methods where each feature is replicated into a high-dimensional feature space. Specifically, the augmented source data contain general and source-specific version, while the augmented target data contain general and target-specific version. The supervised metric learning methods can be used. BIB005 propose a supervised transfer metric learning (TML) using the idea of multi-task metric learning. BIB009 propose a metric hypothesis transfer learning, where a biased regularization is introduced to learn the target metric M by regularizing the difference between the target metric M and the source metric M S . They also provide the theoretical analysis of supervised regularized metric learning approaches. Some methods assume that samples from only a subset of categories are available in the target training set. Then the adapted features are generalized to unseen categories (which are unseen in target dataset, but have seen in source dataset). The reason why the methods under this setting are not discussed under the problem of heterogeneous label spaces is that they still assume the same label spaces between domains, but some of the categories are not presented in the target training set. Generally, these methods assume the shift between domains is category-independent. For example,  propose a supervised metric learning method to learn a transformation that Fig. 1 : A taxonomy of cross-dataset recognition problems minimizes the effect of domain-induced changes in the feature distribution using the target training labelled data from a subset of categories. Then the transformation is applied to unseen target test data that may come from different categories from the target training data. Classifier-based Transfer. The class-based criterion has been used for classifierbased methods that transfer the parameters of discriminative classifiers across datasets. BIB002 propose Adaptive Support Vector Machines (A-SVMs) to adapt one or more source classifiers to the target domain by learning a new decision boundary that is close to the original decision boundary as well as separating the target data. Similarly, BIB003 propose cross-domain SVM (CD-SVM). They also want to preserve the discriminant property of new classifier over source data, which is addressed over important source data samples that have similar distribution to the target data. introduce the adaptive structural SVM (A-SSVM) and structure-aware A-SSVM (SA-SSVM) to adapt classifier parameters between domains by taking the structure knowledge in feature space into account. This problem assumes limited target data are presented in the training stage, see Table VIII . This problem is named supervised heterogeneous domain adaptation. Feature Representation-based Transfer. Some methods assume that the source and target datasets are different only in terms of feature spaces while the distributions are the same between datasets. Since the labelled data in the target dataset are scarce, BIB006 propose to use the auxiliary heterogeneous data that contain both modalities from Web to extract the semantic concept and find the shared latent semantic feature space between different modalities. There are also methods assume that not only the feature spaces are heterogeneous between domains, the data distributions are also diverged, which will be discussed in Section 4.1.3. When limited labelled data (maybe only one example per category) are presented in the target dataset, the problem is generally named one-shot learning or few-shot learning. The characteristics of this problem are shown in Table XI . This setting is closely related to multi-task learning. The difference is that one-shot learning emphasize on the recognition on the target data with limited labelled data while multi-task learning aims at improving all the tasks with good training data in each task. Feature Representation-based Transfer. BIB008 propose a transfer topic model (TTM) by assuming the source and target datasets can be represented by shared concepts even with different classes. Since the target labelled data are insufficient, they propose use the topics learned from the source domain to regularize the topic estimation in the target domain. Kullback Leibler divergences are used as regularization to reduce the shift between topic pairs of the two domains. This problem assumes the labelled target data are available. We name this problem as heterogeneous supervised transfer learning. 6.1.1. Higher-level Representation Criterion. Feature Representation-based Transfer. Rather than assuming completely different feature spaces, propose to transfer the knowledge of RGB-D (RGB and depth) data to the dataset that only has RGB data, and use this additional source of information to recognize human actions from RGB videos. They applied latent low-rank tensor transfer learning to learn shared subspaces of two databases. Specifically, each action sample is represented as a three-order tensor (row dimensions, column dimensions, and number of frames) and the subspace is learned by imposing the low-rank Hybrid knowledge transfer. BIB007 propose to transfer the knowledge between different activity recognition tasks, relaxing the assumption of same feature space, same label space as well as same underlying distribution by automatically learning a mapping between different sensors. They adopt similar idea of translated learning BIB004 ] to find a translator between different feature spaces using statistic criterion (i.e. Jensen-Shannon divergence). Then the Web knowledge is used as a bridge to link the different label spaces using self labelling.