How would you utilize data normalization techniques in preprocessing to enhance the performance of a generative model

Question

Can you tell me How would you utilize data normalization techniques in preprocessing to enhance the performance of a generative model?

Answer 1

Data normalization ensures consistent input distribution, stabilizing training and improving the convergence of generative models.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• astype('float32') / 255.0: Scales pixel values to [0, 1] range for better model performance.

• expand_dims: Makes the data channel-compliant for convolutional models.

• tf.data.Dataset: Efficient input pipeline with batching and shuffling.

• Normalized input leads to faster training, stable gradients, and improved output quality in generative tasks (e.g., VAEs, GANs, Transformers).

Hence, applying normalization in preprocessing enhances model stability and learning efficiency in generative modeling workflows.