## 5 Comparisons of Transfer Learning and Analogical Inference in Two Application Domains
### Computer Vision
The construction of synthetic data opens the doors for historically small datasets to be reasonably expanded without the need for combining datasets (if possible). This is beneficial because models typically show better performance with larger training, test, and/or validation datasets. For various reasons, some datasets are naturally limited to a small size, so by being able to artificially \"expand\" them through synthetic data, more accurate algorithms and models can be designed and implemented. Over the past few decades, computational models of visual reasoning have been developed both in artificial intelligence [51; 70; 71] and cognitive science [5]. Despite many important differences, current models share certain core assumptions that are critical for their successes in accounting for human visual reasoning abilities but also lead to serious limitations. [PERSON] et al. compared human and machine learning methods in a task that required categorizing images of novel shapes based on relational rules regarding the spatial arrangement of constituent parts [77]. The results showed that humans can efficiently acquire relational categories from a handful of exemplar images, but the performance of machine learning algorithms lagged far behind that of humans, even after exposure to thousands of labeled examples. In more recent work, deep learning models were trained using 1 million images generated according to relational rules [78]. For some reasoning problems, mainly involving spatial relations, the deep learning models reached human-level performance; however, for problems involving same-different comparative relations on certain visual attributes, the model performed much worse than humans, even after extensive training. Overall, computational accounts of visual reasoning using machine learning and deep learning approaches have so far fallen short of human-level performance for a wide range of visual reasoning problems, and (unlike humans) they rely on training with many similar problems. In recent years, AI researchers developed visual analogy algorithms to solve visual reasoning problems, such as [PERSON]'s Progressive Matrices (RPM), first discussed in [47] and its extended versions in modern datasets, such as Procedurally Generated Matrices (PGM) [79], RAVEN [49], and Impartial-RAVEN [80]. These analogy problems are like the Bongard Problems originally discussed in [81] and later modernized into their namesake dataset [82]. Visual analogy problems and their real-world applications have been the subjects of a few recent review papers [6; 83; 84]. Though verbal and visual analogies are typically separate domains, there have been instances in which a verbal analogy algorithm was re-utilized for visual problems [85; 86]. [PERSON] et al. applied the Bayesian Analogy with Relational Transformations (BART) model to an A:B::C:D dataset of individual objects with corresponding textual labels. Utilizing a multi-view learning scenario, BART was able to evaluate both the visual and semantic components to derive the correct solution for the given analogy. Many models for solving RPM problems were created with inspiration from human studies, such as the Affine and Set Transformation Induction (ASTI) [87] and ASTI+ [88] algorithms. The idea behind many cognitive science applications is to match human performance, rather than achieve 100% accuracy, as is typically the goal in AI-based transfer learning applications. Posing a few-shot learning scenario using RPM problems, the meta-learning Analogical Contrastive Learning (meta-ACL) algorithm was developed to solve RPM by quantifying relational similarity between a source and target [89]. The meta-ACL algorithm was developed as a few-shot learning algorithm (subset of transfer learning) but has an emphasis on utilizing the relational structure of RPM problems to solve them [89]. This is one case where we can identify a marriage between transfer learning and analogical inference.