## 5 Comparisons of Transfer Learning and Analogical Inference in Two Application Domains
### Computer Vision
The Rel-AIR (Relational Attend-Infer-Repeat) algorithm uses \"unsupervised scene decomposition\" to solve problems with the RAVEN and PGM datasets [90]. Rel-AIR splits elements of a given image into \"segments,\" turns these segments into embeddings, and then reconstructs the image from ground zero until the original image is recreated. This process repeats such that the algorithm continues to learn about all the given information in RPM-like problems to derive what characteristics (such as size, color, and shape) are common. One application that utilizes analogical mapping within zero-shot learning is visiPAM (visual Probabilistic Analogical Mapping), which takes labeled nodes on the images and infers the mapping between the two images [91]. Unlike the other previously mentioned algorithms, which focus on finding an answer (whether the answer must be completely generated or selected from a given list), visiPAM tackles the mapping problem. As mentioned earlier, compared to transfer learning techniques, analogical inference techniques have a greater emphasis on discovering the mapping or relational structure between the elements of two domains. Unlike RPM problems for which there is an inherent, deterministic answer, a mapping may differ based on the respondent, and there is not one distinct answer (as in the case of the problems visualized in Figure 5a,b). In contrast to most current AI models, some models developed in cognitive science are capable of complex reasoning with a small amount of (or even no) training data. These models typically assume access to inputs with the expressive power of structured propositions. Some of the models use a symbolic code based on predicate calculus [57; 92], whereas others employ neural codes that can capture role bindings, such as tensor products [93] or neural synchrony [94; 95]. Structured representations make it possible to find systematic mappings across different contexts and to form relational generalizations. However, with a few important exceptions [82], most cognitive models simply hand-code the structured representations from images and have begged the basic question of where their structured representations come from. In practice, hand-coded representations restrict the applicability of models to relatively small \"toy problems\" (or at least to toy representations of what are much more complex problems). These models are unable to operate on the natural images that people encounter in their daily life. This limitation precludes taking advantage of databases that are now readily available as sources of \"big data\" for computational work. This difference in typical problem size between transfer learning and analogical inference techniques is one of the aspects distinguishing the two. In summary, transfer learning applications in computer vision problems involve the utilization of feature learning or domain adaptation to \"learn\" elements within the training class and apply that to a similar, but new, domain for classification tasks. In contrast, analogical inference methods are focused more on representing relational structures in images and inferring the mapping between the source and the target.