Why is pretraining a deep MLP network considered beneficial?

Pretraining a deep multi-layer perceptron (MLP) network is beneficial primarily because it increases the chances of achieving a global optimum during the training process. When a model is pretrained, it starts with weights that are already informed by a previous learning task. This initialization can help the model escape local minima more effectively during backpropagation.

Deep networks, such as MLPs, can be quite complex with many parameters, making them susceptible to getting trapped in local optima, which are solutions that are better than their immediate neighbors but worse than the best possible solution. Pretraining provides a better starting point, allowing the network to converge towards the global optimum more reliably. This initial condition can guide the optimization process, ensuring that the weights are in a more favorable range to escape local minima.

In addition, pretraining often allows for more efficient learning by reducing the required number of epochs to reach satisfactory levels of performance on the target task. This foundational training usually takes place on large, related datasets, enabling the model to learn general features that are helpful for various tasks, thus enhancing overall model performance.

The other options have their own relevance but do not capture the core advantages of pretraining as effectively as the ability to aid in achieving a global optimum during