Description: Negative sampling is a technique used in the training of machine learning models that aims to improve the efficiency of the learning process by selecting negative examples. In this context, negative examples are those that do not belong to the class of interest and thus help models learn to distinguish between what is relevant and what is not. This technique is particularly useful in situations where positive data is scarce or costly to obtain, while negative data is abundant and easy to collect. By incorporating negative examples into training, models can enhance their generalization ability and reduce the risk of overfitting, as they learn to identify features that differentiate classes. Negative sampling is applied in various areas, including anomaly detection, where it is crucial to identify unusual patterns in large volumes of data, and in generative models, such as Generative Adversarial Networks (GANs), where the goal is to generate data that is indistinguishable from real data. In summary, negative sampling is a valuable strategy that optimizes the learning process by providing a broader and more diverse context for model training.
