What ReLU 0 really mean?

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1 min read

Once you start learning ReLU, you often hear that the reason why ReLU is used is introducing the complexity and optimization of the gradient descent algorithm. However, if you think of the meaning of ReLU 0 value, it works differently as well.

The ReLU activation function can help remove less informative input features by setting their output to 0. When the output of a ReLU unit is 0, it effectively "turns off" that unit and stops any information from flowing through it to the next layer.

This can be useful because it allows the neural network to focus on the more important input features that are more informative for the unit. Having said that, an input feature that may be less informative for a specific ReLU unit could still be informative for other ReLU units in the same layer, or for units in the subsequent layer of the network.

Overall, some input features are informative for some units in layers, and some are not. Therefore, ReLU units turn off uninformative inputs for that specific units.