## 5 Comparisons of Transfer Learning and Analogical Inference in Two Application Domains
### Summary  
There are many similarities between transfer learning and analogical inference, but also several key differences, which are listed in Table 4. The characteristics of focus are transferred knowledge representation, tasks, amount of required training data, and the scope of transfer. Additional characteristics also differentiate applications of analogical inference and transfer learning; however, we believe that these are important to consider. The emphasis on transferred knowledge representations is one of the key differences between transfer learning and analogical inference. Transfer learning is concerned with representing the source and target data by learning joint feature spaces (based on the results from feature extraction) for a particular type of task. A feature space is \(n\)-dimensional; however, for simplicity, a two-dimensional feature space utilizing two arbitrary features, X and Y, is shown in Figure 8. As mentioned earlier, a feature is best thought of as a numerical variable for the given data instance. Utilizing different shapes for the class of each data instance in Figure 8, the feature space can be divided such that there are predicted \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Transfer Learning** & **Analogical Inference** \\ \hline Transfered Knowledge & Feature spaces & Relational structures \\ Representation & & \\ \hline Primary Tasks & Classification & Generation \\  & Generation & Mapping \\  & Retrieval \\ \hline Amount of Required Training Data & Large amounts & Small amounts \\ \hline Scope of Transfer & Near & Near and Far \\ \hline \hline \end{tabular}
\end{table} 
Table 4: High-level comparison between transfer learning and analogical inference. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Cognitive-Science-Inspired**} & \multirow{2}{*}{**Vector Space Models (VSMs)**} & \multirow{2}{*}{**Transformer Language**} \\  & & **Architectures** & & & **Models** \\ \hline  & \(\bullet\) & Structure Mapping & & & \\  & Engine (SME) [92] & & & & \\  & \(\bullet\) & Analogy Constraint & & & \\  & \(\bullet\) & Mapping Engine & & & \\  & (ACME) [120] & & & & \\  & \(\bullet\) & Associative & & & \\  & Memory-based & & & & \\  & \(\bullet\) & Reasoning (AMBR) [121] & & & \\  & \(\bullet\) & Learning and Inferences & & & \\  & with Schemas and & \(\bullet\) & Word2 Vec [96] & & (BERT [102]; [PERSON] [103]; [PERSON] [104]; [PERSON] [105]) \\ Example & & Analogy (LISA) [94] & \(\bullet\) & Global Vectors (GloVe) & & \\  & Distributed & [97] & & & \\ models & Representations for & \(\bullet\) & FastText [98] & & \\  & Analogy MAPper & & & & \\  & (DRAMA) [64] & & & & \\  & Discovery of Relations & & & & \\  & by & & & \\  & \(\bullet\) & Analogy (DORA) [122] & & & \\  & \(\bullet\) & Bayesian Analogy with & & & \\  & Relational & & & \\  & Transformations (BART) & & & \\  & [117] & & & \\ \hline \hline \end{tabular}  
* BART focuses on relationship(s), so it is, therefore, only applicable to word-based analogies.  
\end{table} 
Table 3: _Cont._\"boundaries\" for each data class, represented by different shapes. Hence, knowledge can be transferred in latent feature spaces shared between the source and target domains. In contrast, most algorithms in analogical inference focus on representations based on relational structure and the transfer of such structural knowledge between the source and target domains. Hence, capturing the similarity between relational structures in analogical inference prompts more abstract representations and thinking. A relational structure can be represented as a feature space; however, the feature space is not as essential to applications and methods in analogical inference when compared to transfer leaning. Transfer learning takes a more mathematical approach, involving the vectorization of data (often originally in image or text form) to develop a feature space for the given domain.