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Using a gym env problem with time-series data the model quickly memorizes. I'm looking for ways to minimize it by injecting noise and dropping frames by inserting noise/dropping layers in the model (note link below).
Is there a simple way to do that? An example is more than welcome.
Did you try to look at data augmentation? Is there any way to synthetically generate data in your domain, or synthetically generate more samples from the ones you have?
Hello,
Using a gym env problem with time-series data the model quickly memorizes. I'm looking for ways to minimize it by injecting noise and dropping frames by inserting noise/dropping layers in the model (note link below).
Is there a simple way to do that? An example is more than welcome.
#260 (comment)
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