Description: Label noise refers to the presence of incorrect or misleading labels in a dataset, which can negatively impact the performance of a machine learning model, especially in the context of different types of neural networks. This phenomenon occurs when the labels assigned to images or data do not accurately reflect their real content, leading the model to learn incorrect patterns. For example, if an image of a dog is labeled as a cat, the model may confuse features of both animals, resulting in decreased accuracy in classification. Label noise is a critical issue in model training, as it can introduce biases and errors that propagate throughout the learning process. Identifying and correcting this noise is essential to improve data quality and, consequently, model performance. In the realm of neural networks, which are used for various tasks including computer vision, label noise can be particularly detrimental, as these networks heavily rely on the quality of input data to generalize correctly to new instances. Therefore, addressing label noise is fundamental to ensuring that models are robust and accurate in their predictions.
