## 3 What Is Transfer Learning? Transfer learning became an active research area in the early 1990s, though the technique and its applications date back to the mid-1970s [28]. Transfer learning differs from other traditional machine learning methods because of its focus on the interaction and \"transfer\" of knowledge from a source model to a target model. As shown in Figure 2a, a traditional approach would treat the source and target datasets completely separately and train a separate model for each. In contrast, a transfer learning approach (shown in Figure 2b) would create a model from the source dataset and utilize that knowledge to train a model for the target dataset. There are several benefits to utilizing transfer learning over traditional methods. These benefits include the elimination of the following requirements/expectations of traditional ML methods:  
1. Necessity of sufficiently large amounts of high-quality and labeled training (and testing) data in the target domain [15; 29; 30; 31];
2. Requirement for the training and testing datasets to share the same feature spaces [31];
3. Need for significant human interaction (time and monetary compensation therein) for annotation, verification, etc., during the data curation stage and/or active-learning-like techniques [31]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Learning** & **Description** \\ \hline Deep & A subset of ML that utilizes artificial neural networks (ANNs) with three or more layers, sometimes called “deep neural networks (DNNs)” \\ \hline Supervised & Prediction of a classification or value (in the case of regression) where the data (used for training and testing) are all labeled and known \\ \hline Unsupervised & Utilizes only unlabeled data typically for clustering or association problems \\ \hline Semi-supervised & A hybrid approach between supervised and unsupervised involving labeled and unlabeled data \\ \hline Self-supervised & A type of unsupervised and/or semi-supervised methods where arbitrary labels are created for each data instance allowing it to act as a supervised learning problem \\ \hline Contrastive & Type of self-supervised methods where data instances are compared to one another to determine their clustering into classes \\ \hline Reinforcement & Approach utilizing a reward/penalty system upon evaluation of the environment and its goal(s) \\ \hline Multi-view & An instance where a specific problem can be accurately viewed in two different manners \\ \hline Multi-task & Technique focused on solving multiple related problems/tasks simultaneously \\ \hline Ensemble & A method that combines individual models into a larger model to improve performance \\ \hline Active & A specific type of semi-supervised learning where an “oracle,” commonly a human, is used to \\  & derive information about or assess the algorithm and/or its performance \\ \hline Online & A method that is best applied when the data to train the algorithm is introduced sequentially as the algorithm’s parameters are fine-tuned \\ \hline Zero-shot & Type of learning where an algorithm has “unseen” data outside of its initial training dataset with the ability to accurately identify and classify said unseen data \\ \hline Few-shot/low-shot/one-shot/multi-shot & A semi-supervised approach that uses a very small dataset of labeled instances (or only one instance in the case of one-shot) \\ \hline Transfer & A technique where some portion of knowledge from one domain is transferred and applied to a \\  & different but related domain \\ \hline Analogical inference & Method where an analogy is formed between two different but related domains to draw \\  & inferences between each other as well as specifically the target domain \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types of machine learning methods. However, transfer learning is not a \"one-size-fits-all\" technique due to its underlying assumptions. Any time the transfer of knowledge occurs, there are assumptions regarding how transferable the knowledge is from the source to the target domain.