What are the key differences between batch normalisation and instance normalisation?

Question Analysis

This question asks about the differences between two normalization techniques used in machine learning, particularly in deep learning models: batch normalization and instance normalization. Understanding these concepts is crucial for improving model training and performance. It's important to be familiar with how each technique operates and the specific scenarios where one might be preferred over the other.

Answer

Batch Normalization and Instance Normalization are both techniques used to stabilize and accelerate the training of deep learning models by normalizing the inputs to each layer.

Key Differences:

• Normalization Scope:

  • Batch Normalization: Normalizes the inputs across the entire mini-batch. This means it computes the mean and variance for each feature across all examples in a mini-batch.
  • Instance Normalization: Normalizes each instance separately. For each individual example, it computes the mean and variance across spatial dimensions.

• Use Case:

  • Batch Normalization: Commonly used in fully connected and convolutional neural networks where the entire batch can be used to calculate statistics, making it suitable for applications where batch size is consistent and relatively large.
  • Instance Normalization: Often used in style transfer and image generation tasks. It is particularly effective in scenarios where the style or appearance of each instance is unique and should be preserved.

• Effect on Model:

  • Batch Normalization: Helps in reducing internal covariate shift and allows for using higher learning rates. It also provides some regularization, reducing the need for Dropout.
  • Instance Normalization: Offers more flexibility in adjusting to individual sample statistics, making it beneficial for tasks requiring precise style information.

• Dependency:

  • Batch Normalization: Relies on the batch size, which can affect performance if the batch size is too small.
  • Instance Normalization: Independent of batch size, making it more stable when varying batch sizes.

By understanding these differences, you can choose the appropriate normalization technique based on the specific requirements of your neural network model and the task at hand.