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tysam_and_co t1_jb2kgo2 wrote

Reply to comment by tysam_and_co in [R] [N] Dropout Reduces Underfitting - Liu et al. by radi-cho

Hold on a minute. On reading through the paper again, this section stood out to me:

>Bias-variance tradeoff. This analysis at early training can be viewed through the lens of the bias-variance tradeoff. For no-dropout models, an SGD mini-batch provides an unbiased estimate of the whole-dataset gradient because the expectation of the mini-batch gradient is equal to the whole-dataset gradient. However, with dropout, the estimate becomes more or less biased, as the mini-batch gradients are generated by different sub-networks, whose expected gradi- ent may not match the full network’s gradient. Nevertheless, the gradient variance is significantly reduced, leading to a reduction in gradient error. Intuitively, this reduction in variance and error helps prevent the model from overfitting to specific batches, especially during the early stages of training when the model is undergoing significant changes

Isn't this backwards? It's because of dropout that we should receive _less_ information from each iteration update, which means that we should be _increasing_ the variance of the model with respect to the data, not decreasing it. We've seen in the past that dropout greatly increases the norm of the gradients over training -- more variance. And we can't possibly add more bias to our training data with random I.I.D. noise, right? Shouldn't this effectively slow down the optimization of the network during the critical period, allowing it to integrate over _more_ data, so now it is a better estimator of the underlying dataset?

I'm very confused right now.

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amhotw t1_jb38ai5 wrote

Based on what you copied: they are saying that dropout introduces bias. Hence, it reduces the variance.

Here is why it might be bothering you: bias-variance trade-off makes sense if you are on the efficient frontier, ie cramer-rao bound should hold with equality for trade-off to make sense. You can always have a model with a higher bias AND a higher variance; introducing bias doesn't necessarily reduce the variance.

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tysam_and_co t1_jb3i6eq wrote

Right, right, right, though I don't see how dropout introduces bias into the network. Sure, we're subsampling the network in general, but overall the information integrated with respect to a minibatch should be less on the whole due to gradient noise, right? So the bias should be less and as a result we have more uncertainty, then more steps equals more integration time of course and on we go from there towards that elusive less-biased estimator.

I guess the sticking point is _how_ they're saying that dropout induces bias. I feel like fitting quickly in a non-regularized setting has more bias by default, because I believe the 0-centered noise should end up diluting the loss signal. I think. Right? I find this all very strange.

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Hiitstyty t1_jb4wjnj wrote

It helps to think of the bias-variance trade off in terms of the hypothesis space. Dropout trains subnetworks at every iteration. The hypothesis space of the full network will always contain (and be larger) than the hypothesis space of any subnetwork, because the full network has greater expressive capacity. Thus, the full network can not be any less biased than any subnetwork. However, any subnetwork will have reduced variance because of its smaller relative hypothesis space. Thus, dropout helps because its reduction in variance offsets its increase in bias. However, as the dropout proportion is set increasingly higher, eventually the bias will be too great to overcome.

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