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I'm not sure how just training the batch normalisation layer, you can get an accuracy of 83%. The batch normalisation layer parameters $\gamma^{(k)}$ and $\beta^{(k)}$, are used to scale and shift the normalised batch outputs. These parameters are learnt during the back-propagation step. For the $k$th layer, y^{(k)} = \gamma^{(k)}\hat{x}^{(k)} + \beta^{(k)}...

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A batch normalisation layer is like a standard FC layer but instead of learning weights and bias', you learn means and variances and scale the whole layer by said means and variances. Fact 1: Because it behaves just like a normal layer, and can learn, with the right structure it will learn to get a high enough accuracy. Fact 2 Disabling learning on a batch ...

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I think there is no simple way to transfer knowledge changes between different models. If you take your initial model and create a new version of it which you use to learn some other task (like "Walk to a specific location"), then the values copied from the first (original) model change in the second model. From that moment on, training the former ...

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