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Questions tagged [dropout]

Dropout is a technique to reduce overfitting during the training phase of a neural network.

Dropout is a regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data. The term "dropout" refers to dropping out units (both hidden and visible) in a neural network.

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How exactly does DropOut work with convolutional layers?

Dropout (paper, explanation) sets the output of some neurons to zero. So for a MLP, you could have the following architecture for the Iris flower dataset: 4 : 50 (tanh) : dropout (0.5) : 20 (tanh) : 3 (softmax) It would work like…
Martin Thoma
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Dropout implies stochastic descent?

The question is very simple, yet I can't find a quick confirmation on the web. It might seem obvious - by design, will Dropout always result in stochastic-looking gradient descent? (SGD) I've built a system which converges nicely with momentum at 0…
Kari
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Dropout dividing by compensation term = overshoots the result?

When applying dropout mask, why is it acceptable to divide the resulting state by the percentage of survived neurons? I understand that it's to prevent signal from dying out. But I've done the test, and found that it disproportionally magnifies the…
Kari
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The idea behind Dropout

I just need to check my understanding regarding the dropout regularization technique. According to my understanding, neurons can interact with each other in a way that they can fix each other's mistakes. Therefore, this can make the model learns the…
John adams
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