In the context of deep learning, which of the following scenarios best exemplifies the application of the residual learning framework to improve neural network training efficiency?

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A neural network that uses activation functions only in the output layer.

A deeper neural network that learns to predict the difference between the actual output and the output from the previous layer.

A shallow neural network that adds more neurons to improve accuracy.

A recurrent neural network that processes sequences without using any shortcuts.

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Answer

Residual learning is used when a very deep neural network starts to fail because its layers cannot learn useful features and gradients vanish. In this framework, each block learns a small correction to an identity mapping, so the network can focus on learning residuals rather than the whole function. A clear example is a 100‑layer ResNet trained on ImageNet: without skip connections the network barely converges, but adding shortcut connections lets each block learn only the difference from its input, speeding up training and improving accuracy. This scenario shows how residual learning improves training efficiency by stabilizing gradients and simplifying the learning task.

Detailed Explanation

Residual learning lets a deep network focus on learning the small difference between what the previous layer produced and the desired output. Other options are incorrect because Using activation functions only in the output layer means every hidden layer is linear; Adding more neurons to a shallow network increases its width, not its depth.

Key Concepts

Residual Learning Framework

Deep Neural Networks

Optimization in Neural Networks

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Residual Learning Framework

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easy level question

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