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:  Transfer learning 
1. Abstract of Cited Paper (BIB001):  Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.

Literature Review:
Transfer learning (TL)  BIB001 BIB002 aims to produce an effective model for a target task with limited or no labeled training data by leveraging and exploiting knowledge from a different, but related source domain to predict the truth label for an unseen target instance. Due to insufficient labeled instances, training a model in this target task would result in degraded performance as compared to a model trained with sufficient labeled data. However, by enhancing the training with supplementary labeled data from a related source domain, the model's ability to classify target instances can be improved. The challenge becomes how to distinguish beneficial knowledge in a source domain from the inherent cross-domain noise, due to the varied distributions, and apply it to a target domain. Transfer learning can be split into two main categories when it comes to the feature spaces: homogeneous and heterogeneous transfer learning.