Zero-shot learning and one-shot learning represent two distinct paradigms within the realm of machine learning, each characterized by its approach to handling unseen or limited training data. Zero-shot learning entails training a model to perform tasks for which it has not been explicitly provided with labeled examples or training data. This is achieved by leveraging prior knowledge or transfer learning from related tasks or domains. In contrast, one-shot learning involves training a model on a single example or a small number of labeled instances for a given task, thereby enabling the model to generalize and make predictions based on limited data.
