What is the principle and purpose of dropout?

Dropout is a commonly used regularization technique in neural networks, where during training, a certain percentage of neuron outputs are set to 0 randomly, effectively “dropping out” some neurons. This technique works by forcing the model to not rely on specific neurons during training, reducing co-adaptation between neurons and ultimately increasing the model’s generalization ability.

Specifically, Dropout can help address overfitting issues and improve the model’s generalization ability. By randomly dropping a portion of neurons during training, Dropout can reduce dependencies between neurons, preventing the model from relying too heavily on specific neurons and lowering the risk of overfitting to certain inputs. Additionally, because different neurons are randomly dropped each time during training, Dropout can be seen as conducting multiple samples on the training data, thereby enhancing the robustness of the model.

In conclusion, the principle of Dropout involves randomly dropping the output of neurons to reduce co-adaptation between neurons, ultimately improving a model’s ability to generalize and address overfitting issues while enhancing its robustness.