Is it standard to save the noise distribution your model trained on to use and use the same values for transfer learning / fine tuning / inference?

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Or do people typically create a new distribution of noise for the model to learn at each new application?

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I’m assuming that during one entire training session you create the noise distribution once so that the model is learning the function of turning that noise into features of the images right?

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Thanks in advance!