Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Introducing skip connections can help gradients flow better during training.
B
Simply increasing the number of neurons in a layer always improves performance.
C
Using batch normalization can reduce internal covariate shift and improve training dynamics.
D
Residual networks (ResNets) allow deeper architectures without suffering from degradation.
E
Training with larger datasets eliminates the need for architectural innovations.
Understanding the Answer
Let's break down why this is correct
Answer
Degradation in very deep networks is often fixed by adding shortcut or residual connections that let the gradient flow directly, so a layer can simply learn an identity mapping if needed. Batch‑normalization is also useful because it stabilizes the distributions of activations and speeds up training, making it easier for deep models to converge. Careful weight initialization and using a smaller learning rate help keep the signal from vanishing or exploding. A tiny concrete example: a 50‑layer ResNet adds a 1×1 convolution shortcut so each block can pass its input unchanged while still learning useful features. These techniques together prevent the loss from increasing as the network gets deeper.
Detailed Explanation
Skip connections let the gradient skip layers, so the signal can travel farther and the network learns easier. Other options are incorrect because Adding more neurons can make the model too complex and cause it to memorize training data instead of learning useful patterns; Even with a huge amount of data, the network still needs a good design to learn efficiently.
Key Concepts
Degradation Problem in Deep Networks
Residual Learning
Batch Normalization
Topic
Degradation Problem in Deep Networks
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1What is the primary issue associated with the degradation problem in deep networks, and how can empirical validation help mitigate this issue?
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2
Question 2How do residual connections in deep networks enhance training efficiency and mitigate the degradation problem?
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3
Question 3How does increasing the depth of a deep network potentially impact its performance metrics, particularly in terms of the degradation problem?
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4
Question 4Which of the following strategies most effectively addresses the degradation problem in deep networks while considering training efficiency, optimization techniques, and scalability issues?
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5
Question 5Degradation Problem in Deep Networks : Performance degradation :: Residual Learning : ?
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6
Question 6Arrange the following steps in order to effectively address the degradation problem in deep networks: A) Implement residual connections, B) Increase network depth, C) Monitor training accuracy, D) Adjust learning rates accordingly.
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7
Question 7When considering the degradation problem in deep networks, which of the following scenarios best illustrates an approach to mitigate this issue?
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8
Question 8The degradation problem in deep networks primarily refers to the issue where increasing network depth leads to performance ____, rather than overfitting.
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9
Question 9What is the primary cause of the degradation problem in deep networks as they increase in depth?
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