How do you use unsupervised pre-training to enhance the performance of generative models

Question

Can you explain, using code, how to use unsupervised pre-training to enhance the performance of generative models?

Answer 1

You can use unsupervised pre-training to enhance the performance of generative models by referring to the Pytorch implementation below. Where to improve generative models, we are initializing with a strong understanding of patterns before fine-tuning a specific task 

• Pre-train: The model unsupervised on unlabeled data (e.g., using autoencoders or masked predictions)

The code snippet below explains the pre-trained autoencoder or masked model:

• Fine-Tune: The pre-trained model on the target task with labeled data(e.g., For conditional generation)

The code snippet below explains the fine-tuned labeled data.

Using the above implementations, you can improve model performance by increasing learned features from the unsupervised phase, speeding up convergence, and enhancing generation quality.