Training Neural Networks With Noise
In the context of the neural network, noise can be defined as random or unwanted data that interrupts the model’s ability to detect the target patterns or relationships. In some instances, noise can adversely impact the efficient learning capability of a model which tends to provide decreased performance and reduce the model’s accuracy.
However, adding a little noise can improve neural network performance. By introducing randomness during training, known as noise injection, acts like a magic potion for the models.
When the dataset is small we tend to have very few samples, there arises the problem of mapping input and output data, which limits the model’s ability to learn the training data and consequently leads to poor performance.
Train Neural Networks With Noise to Reduce Overfitting
Neural networks have revolutionized artificial intelligence but they often fall into the trap of overfitting which may potentially reduce the model’s accuracy and reliability.
To address this issue, we will be uncovering the noise-based regularization technique, that can help us to reduce overfitting.
Table of Content
- Training Neural Networks With Noise
- Noise Injection Techniques
- Implementation: Training Neural Network with Noise