Description: Noise injection is a technique used to improve the robustness of neural networks by adding noise to the input data during training. This strategy aims to simulate variations in real data, allowing the model to learn to generalize better and be less sensitive to small perturbations in the data. By introducing noise, it prevents the model from overfitting to specific patterns in the training data, which can lead to poor performance on unseen data. Noise injection can take various forms, such as adding Gaussian noise, altering input features, or modifying labels. This technique is particularly relevant in the context of deep learning and machine learning, where variability in data can be crucial for generating more realistic and varied results. Additionally, noise injection is used in anomaly detection, helping models identify unusual patterns in data by making them more robust against unexpected variations. In summary, noise injection is a valuable tool in the machine learning arsenal, contributing to the creation of more efficient and reliable models.
