Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It allows the training of much deeper networks without suffering from vanishing gradients.
B
It eliminates the need for activation functions in neural networks.
C
It reformulates layers to learn residual functions, improving the network's ability to optimize.
D
It increases the complexity of the model, making it harder to interpret.
E
It helps mitigate the degradation problem associated with very deep networks.
Understanding the Answer
Let's break down why this is correct
Answer
Residual learning lets a network learn the difference between the desired output and the input rather than the whole mapping, which makes it easier for deep layers to adjust their weights and avoids the problem of gradients disappearing in very deep nets. Because the shortcut connections pass the input unchanged, the network can preserve useful information while still refining it, so training becomes faster and more stable. This framework also allows the construction of much deeper models that still converge, improving accuracy on tasks like image recognition. For example, a 50‑layer ResNet can learn to correct a shallow 5‑layer network’s errors, leading to higher classification rates on ImageNet. Overall, residual learning simplifies training, supports deeper architectures, and typically yields better performance.
Detailed Explanation
Residual learning lets a network add a shortcut that skips some layers. Other options are incorrect because The shortcut does not remove activation functions; Adding shortcuts does not make the model harder to understand.
Key Concepts
Residual Learning Framework
Deep Neural Networks
Optimization Techniques
Topic
Residual Learning Framework
Difficulty
medium level question
Cognitive Level
understand
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