In the best scenario when adding a ResNet block it will learn to approximate the identity function after a lot of training, helping improve the overall performance of the network. True/False?
Question
In the best scenario when adding a ResNet block it will learn to approximate the identity function after a lot of training, helping improve the overall performance of the network. True/False?
Solution
True. The ResNet block, or Residual block, is designed to learn the residual mapping (the difference between the input and output) rather than the original mapping. In the best scenario, if the optimal function is closer to the identity function, the ResNet block will learn to approximate the identity function after a lot of training. This helps to mitigate the problem of vanishing/exploding gradients in deep networks, thus improving the overall performance of the network.
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Adding a ResNet block to the end of a network makes it deeper. Which of the following is true?
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Question 3Training a deeper network (for example, adding additional layers to the network) allows the network to fit more complex functions and thus almost always results in lower training error. For this question, assume we’re referring to “plain” networks.1 point
Training a deeper network (for example, adding additional layers to the network) allows the network to fit more complex functions and thus almost always results in lower training error. For this question, assume we’re referring to “plain” networks.
The motivation of Residual Networks is that very deep networks are so good at fitting complex functions that when training them we almost always overfit the training data. True/False?
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