Dropout

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Dropout

Dropout is a regularization technique used in neural networks to prevent overfitting. It works by randomly “dropping out” a fraction of neurons during training, meaning that their outputs are set to zero. This process forces the network to learn more robust features that are not reliant on any specific neuron, thereby improving generalization to unseen data.

During training, dropout is applied with a specified probability, which determines the fraction of neurons to be dropped. For instance, a dropout rate of 0.5 means that approximately half of the neurons in a layer will be randomly deactivated during each training iteration. Importantly, dropout is only active during training; during inference or testing, all neurons are used, ensuring that the model can leverage all learned features ²³.

To maintain consistency between training and inference, the outputs of the remaining active neurons are scaled during training. This scaling ensures that the expected value of the activations remains the same during both phases, allowing for coherent performance ⁴. Overall, dropout is a simple yet effective method to enhance the performance of neural networks.

PyTorch documentation

During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution. Each channel will be zeroed out independently on every forward...

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Randomly zero out entire channels (a channel is a 1D feature map, e.g., the j j j -th channel of the i i i -th sample in the batched input is a 1D tensor input [ i , j ] \text{input}[i, j] input [ i ,...

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