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 review of single-source unsupervised domain adaptation 
Chapter Title:  Search space shrinkage 
1. Abstract of Cited Paper (BIB001):  The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.

Literature Review:
In a number of methods the source domain is a means of narrowing the search space. For example, Empirical Bayes approaches fit the prior distribution on data from a source domain. This informed prior assigns low probabilities to parameters that would not be suitable for the current task. Hence, the search space is effectively smaller than the noninformative flat prior. Another example is fine-tuning in deep neural networks. A neural network is first trained on a large generic data set such as ImageNet BIB002 and then trained on the smaller data set of your current problem. Instead of starting from scratch, pre-training already provides sensible parameters  . During the second phase of training, the network does not need to evaluate the loss of the randomly initialized parameters. Ignoring sets of parameters that have not been relevant in similar tasks is an intuitive notion of how the source domain assists the learning process. One immediately understands why it could be dangerous as well. If the source domain informs the classifier to ignore parameters that are actually useful for the target domain, then it interferes with learning. This interpretation shows an interesting link to natural intelligence, where agents use knowledge from previous tasks to complete new tasks BIB001 .