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: A survey on heterogeneous transfer learning 
Chapter Title:  TTI 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels. Therefore, the relationships between the images and text features also provide additional hints for the classification process in terms of the image feature transformations which provide the most effective results. The semantic challenges of image features are glaringly evident, when we attempt to recognize complex abstract concepts, and the visual features often fail to discriminate such concepts. However, the copious availability of bridging relationships between text and images in the context of web and social network data can be used in order to design for effective classifiers for image data. One of our goals in this paper is to develop a mathematical model for the functional relationships between text and image features, so as indirectly transfer semantic knowledge through feature transformations. This feature transformation is accomplished by mapping instances from different domains into a common space of unspecific topics. This is used as a bridge to semantically connect the two heterogeneous spaces. This is also helpful for the cases where little image data is available for the classification process. We evaluate our knowledge transfer techniques on an image classification task with labeled text corpora and show the effectiveness with respect to competing algorithms.

Literature Review:
Qi et al. BIB003 proposed an HTL method for image classification. This method uses a Translator for Text to Images (TTI). TTI uses labeled source text data, text-image cooccurrence data, and limited labeled image target data. Image classification currently has two challenges: (1) labeled image data is relatively scarce and expensive to collect and (2) features of image data lack semantic meaning for class prediction as they represent visual features rather than conceptual ones. On the other hand, labeled text data is often more available than labeled image data and text features have more semantic meaning for predicting a class label. With this observation, this method proposes using transfer learning to exploit such text data to improve image classification. The issue becomes how to relate the text to the images for semantic knowledge transfer. To close this gap, this method uses a text-image co-occurrence matrix which contains images along with the text that occurred with them on the same webpage. Co-occurrence information is effective in this case because of the assumption that the text around an image is describing the concepts in such image. This co-occurrence information is relatively inexpensive to collect and serves as a bridge to learn the correspondence for translating the semantic information between the features of the text source domain to that of the image target domain. This translation is done through a form of a feature transformation called a "semantic translator function. " This translator takes into account the source, target, and co-occurrence data and learns the correspondence between the text from the source and the images of the target through the co-occurrence bridge. Each translator for the source text contains a "topic space" which is a common subspace to associate the data for translation. This translator function is optimized through proximal gradient based optimization to find effective transformation matrices and complies with the parsimony principle. This principle states that the least complex, effective model is preferred as this avoids overfitting issues when faced with low training samples. Figure 3 , adapted from BIB003 , illustrates the semantic label propagation from the source texts to the test image. As shown in the figure, each of the translators are aggregated to create a final prediction function f(x (t) ). Experiments were conducted for image classification and the proposed method is compared against HTLIC BIB002 , TLRisk BIB001 , and a baseline classifier trained only on the images without any source knowledge. The proposed TTI had the best performance in most cases, even when the number of labeled target samples were reduced.