How can progressive resizing be used to optimize training in Generative AI pipelines

Question

With the help of code can you tell me How can progressive resizing be used to optimize training in Generative AI pipelines?

Answer 1

Progressive resizing is a technique where the model is first trained on smaller images and gradually trained on larger ones. 

• This optimizes training by allowing the model to learn basic features at a smaller scale before tackling the complexity of larger data, reducing computational load and speeding up convergence.

Here is the code snippet you can refer to:

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

• Resize Images Incrementally: Images start from a smaller size (28x28), progressively increasing in size at each step.
• Training on Increasing Complexity: As the model gets more capable, it handles larger images, improving the ability to learn detailed features.
• Efficiency: This method reduces computational costs during early training phases and accelerates convergence.

Hence, progressive resizing optimizes training by gradually increasing input complexity, improving both efficiency and model performance.