Nowadays, constructing a large-scale dataset is the prerequisite to achieving the task in our hands. Sometimes the task is a niche, and it would be too expensive or even not possible to construct a large-scale dataset for it to train an entire model from scratch. Do we need to train a model from scratch in all cases? Imagine we would like to detect a certain animal, let's say an otter, in images. We first need to collect many otter images and construct a training dataset.

Deep Learning (DL) advances have cleared the way for intriguing new applications and are influencing the future of Artificial Intelligence (AI) technology. However, a typical concern for DL models is their explainability, as experts commonly agree that Neural Networks (NNs) function as black boxes. We do not precisely know what happens inside, but we know that the given input is somehow processed, and as a result, we obtain something as output. For this reason, DL models can often be difficult to understand or interpret. Understanding why a model makes certain predictions or how to improve it can be challenging.