How do I train a cascaded Generative AI network where intermediate models feed features or outputs into subsequent models for improved generation

Question

Can i know How do I train a cascaded Generative AI network where intermediate models feed features or outputs into subsequent models for improved generation?

Answer 1

Training a cascaded Generative AI network involves sequentially training intermediate models, passing extracted features or generated outputs to subsequent models, and optimizing them jointly or progressively for improved generation.

Here is the code snippet you can refer to:

In the above code we are using the following approaches:

• ModelA (Feature Extractor): Extracts meaningful representations from input data.
• ModelB (Generative Model): Uses extracted features to improve generative outputs.
• Cascaded Architecture: Passes intermediate outputs from ModelA to ModelB for enhanced generation.
• Efficient Training: Allows separate or joint training for improved learning.

Hence, a cascaded Generative AI network enhances generation quality by progressively refining features through multiple models trained in sequence or jointly.