## 5 Comparisons of Transfer Learning and Analogical Inference in Two Application Domains
### Summary
These feature spaces may need to be adjusted when connecting or adjoining feature spaces for two domains to make them comparable. In transfer learning, any semantic information is represented in the data vectors; however, analogical inference may also represent data as a feature space. Analogical inference may also vectorize the data, but there is an emphasis on deriving semantic and relational meanings linking the two domains to make inferences. Both transfer learning and analogical inference can be used for classification (placing in alike groups) and generation (creation of something new within the target domain); however, analogical inference can perform additional tasks, including mapping (drawing correspondences between the source and target domains) and retrieval (accessing short-term and long-term memory for a source domain given the target domain). Although retrieval, to some degree, occurs in all scenarios, analogical inference has more of a focus on what best-fitting instance should be retrieved. Retrieval for analogical inference is relatively flexible, whereas in transfer learning, the retrieval process is more deterministic. In some cases, these general tasks are carried out consecutively to fit the main task at hand. Specifically, in the computer vision realm, transfer learning applications predominantly are for image classification and generation, but analogical inference applications are concerned with solving analogy problems through the selection (more common) or generation (less common) of the \"missing\" element, as shown in Figure 6a,b. Within natural language processing, transfer learning is predominantly concerned with prediction tasks to generate human-like text and language, but semantic similarity between words is also extracted and can be used for solving some verbal analogy problems. Analogical inference is more concerned with solving a broader variety of analogy problems (encompassing sentence- and story-based analogies) in addition to word-based analogies. Analogical inference emphasizes forming representations for relations and concepts by minimizing the need for training with big data and discovering the similarity of relational structures across domains. Examples of some general and specific tasks are shown in Figure 9. Many of the exclusive transfer learning tasks and examples shown in Figure 9 concern utilizing either part of or all of a pre-trained model. Figure 8: Example of a two-dimensional feature space. Applications in the computer vision and natural language processing domains can be classified within the general tasks in Figure 9. The tasks discussed in Table 5 are examples; however, many more exist. It is important to note that transfer learning and analogical inference examples are not necessarily exclusive, and some applications can be both, depending on the algorithm and method(s) utilized. Transfer learning almost always requires the source domain to have a large amount of high-quality labeled data, which are used to build a large, supervised model (such as ImageNet [123] for images or large text corpora for language). When classifications differ between the source and target domains, training data in target domains are necessary for transfer learning. For analogical inference, some knowledge is needed about the source domain, but the technique has also been successful with little or minimum training in the source domain (as in the case of [PERSON]'s Progressive Matrices problems [49]). In many problems related to analogical reasoning, humans do not receive training in the target domain and are unaware of what tasks may be performed in the target domain. Finally, the scope of transfer concerns the information gap between the two domains. Knowledge about differences or similarities between the target and source domains pertains to their representations in feature spaces. Current transfer learning applications are centered on near transfer, which typically occurs when the source and target datasets are within the same general domain.