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Is performing batch regularization in addition to L2 regularization redundant?

Batch regularization: http://arxiv.org/abs/1502.03167

Notice how when performing batch regularization, you forgo using a bias term (and instead normalize your layer output, followed by scaling with a 'scale' term, and shifting with a 'beta' term. I was under the impression that L2 regularization punishes large weights & biases... but if I'm not using biases because of batch regularization, then would I just punish the weights with L2 regularization?

Thanks--

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No. They are not equivalent. They are two different techniques, that each provide some kind of regularization, but using one doesn't necessarily make the other redundant. For instance, in the paper you cite, the authors are using both (see Section 4.2.1 for mention of L2 regularization).

Specifically: whether or not it is more effective to use just batch regularization or to use both will probably depend on the specific learning task you have in mind. But there is no theory that implies the two are equivalent.

Batch normalization still has a bias term that is added after the normalization step (the $\beta$); it does not eliminate it. L2 regularization penalizes large weights and large biases. As far as I can tell, batch regularization doesn't try to do either (at least not explicitly).

When using L2 regularization with batch regularization, I imagine you could either L2-penalize just the weights; or L2-penalize the weights, the $\gamma$ terms (scaling), and the $\beta$ terms (shift). I don't know which will produce better results. My intuition says the latter might be preferable, but experiments and data beat intuition any day.

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  • $\begingroup$ Thanks. I'll try to L2-penalize the scale and beta terms and see how things turn out. $\endgroup$ – sir_thursday Jul 11 '16 at 20:39
  • $\begingroup$ Did you see the result? I am just curious about it. $\endgroup$ – jakeoung Feb 23 '17 at 6:44
  • $\begingroup$ I'm curious about the same thing. $\endgroup$ – kRazzy R May 18 '18 at 20:23

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