What is the key advantage of using residual connections in neural networks?

The key advantage of using residual connections in neural networks is that they improve the flow of information through the network. Residual connections allow the output of a layer to be added directly to the input of a subsequent layer. This approach helps mitigate issues related to vanishing gradients, particularly in deep networks where the signal may weaken as it propagates through multiple layers. By providing a shortcut for gradients during backpropagation, residual connections enable more effective learning and help the model to maintain performance as it grows in depth. This design facilitates the training of deeper models, leading to improved accuracy and performance on various tasks.

Other options do not accurately capture the role of residual connections; for instance, while simplifying the model architecture may have certain benefits, residual connections primarily focus on enhancing the network's ability to learn effectively. They do not eliminate the need for activation functions, which are still crucial for introducing non-linearities into the model. Lastly, while effective training techniques can contribute to better model performance, residual connections do not directly enhance data visualization capabilities.