Challenges and Limitations of Few-Shot and Zero-Shot Learning

1. Introduction

In an era of increasing interest in church the fast developing fields of machine learning and artificial intelligence (AI), one must find a proper solution to missing data. Today, few-shot learning as well as zero shot have become two hot topics. Few-Shot Learning means to used, as little labelled data as possible in training a model. Accuracy with traditional machine learning models must require a huge number of labelled data. Collecting this can be expensive and time consumording. On the other hand, few-shot learning aims to improve efficiency and adaptability by enabling AI models to provide reliable predictions with very few examples (J. Chen, 2023). These are achieved by using contemporary techniques like as meta-learning, embedding learning, and transfer learning that help the model generalize from sparse datasets to new data. On the other hand, Zero-Shot Learning exten ds enormously the frontiers of learning with few data. In the case of zero-shot learning, however, the model has to make predictions on classes it didn't see during training. This is achieved by, during training, learning something from attending one set of classes, and then using it to figure out something about another that was missing (i.e., a completely different class of things). To the end of making inferences based on what it understands from descriptions or attributes, the model usually uses this method of understanding and use. It does not need to have any direct experience.