# Dropout on inputs instead on outputs = DropConnect?

Is dropping out parts of the Input vector better than dropping out parts of the Output vector?

The latter literally makes this same neuron invisible to any further layers. On the contrary, ignoring pieces of the input means some of the further neurons will be able to see this neuron.

Is masking the input pretty much the same as masking the weights (aka DropConnect) and thus gives a higher quality regularization?

• Why will you drop your input initially? – Aditya May 26 '18 at 8:55

Moreover, it is also not good to add dropout in the convolutional layers because they are feature extractors and they are significant features for classification problems. If you miss them, means that you are losing information more than usual. Consider the point that your input to the network is already resized to a smaller shape than its original shape, for instance, the input shape of typical CNNs is 224 * 224 while the original shape may be ten times bigger or even more for each direction.