How does the batch normalization technique work in a CNN?

Batch Normalization is a technique used in deep learning to improve the generalization performance of neural networks by reducing overfitting. It involves transforming the input data into a lower-dimensional feature space while preserving its statistical properties.

How it works:

1. Mean and Standard deviation calculation:

   • For each channel in the input data, the mean and standard deviation are calculated across all the training examples.
   • The mean is the average of the pixel values, and the standard deviation is the square root of the average of the squared differences between each pixel and the mean.

2. Normalization:

   • For each pixel in the input data, the mean and standard deviation are used to normalize the pixel value by subtracting the mean and adding the standard deviation.
   • This process effectively maps the pixel values into a lower-dimensional feature space while preserving their statistical properties.

3. Batch operation:

   • The mean and standard deviation are calculated over all the pixels in a batch of data.
   • These statistics are then used to normalize all the pixel values in the batch.

Benefits of Batch Normalization:

• Reduced overfitting: By reducing the dimensionality of the feature space, batch normalization helps to prevent the neural network from overfitting to the training data.
• Improved generalization performance: The lower-dimensional feature space allows the neural network to learn more generalizable features, leading to improved performance on unseen data.
• Robustness to noise: Batch normalization is less sensitive to noise in the input data compared to other normalization techniques.

Note:

• The batch size is a hyperparameter that controls the size of the batch.
• The batch normalization technique can be applied to both convolutional and fully connected layers.
• It is a widely used technique in deep learning for improving the generalization performance of neural networks.