What methods are effective for adaptive sampling to improve training efficiency in generative models

Question

Can you tell me, with the help of code, the effective methods for adaptive sampling to improve training efficiency in generative models?

Answer 1

You can refer to the following methods, which are effective for adaptive sampling and can improve training efficiency in generative models.

Importance Sampling: You can adjust sampling based on data relevance, focusing on samples with higher error or significance.

The code snippet below shows how it is done:

Curriculum Learning: Starts with simpler samples, and then it gradually increases complexity as the model learns.

The code snippet below shows how it is done:

Uncertainty-Based Sampling: Prioritizes samples where the model shows high uncertainty, encouraging better learning on hard samples.

The code snippet below shows how it is done:

Hence, using these methods is effective for adaptive sampling and improving training efficiency in generative models.