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

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Can you tell me, with the help of code, the effective methods for adaptive sampling to improve training efficiency in generative models?
Nov 13 in Generative AI by Ashutosh
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1 answer to this question.

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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.

answered Nov 13 by nidhi jha

edited Nov 13 by Ashutosh

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