Which of the following is a primary challenge in training very deep convolutional neural networks?Group of answer choicesInsufficient hardwareVanishing gradientsOver-regularizationLack of data
Question
Which of the following is a primary challenge in training very deep convolutional neural networks?Group of answer choicesInsufficient hardwareVanishing gradientsOver-regularizationLack of data
Solution
The primary challenge in training very deep convolutional neural networks is Vanishing gradients.
Here's why:
When training a deep neural network with gradient-based learning methods, there's a difficulty known as the vanishing gradients problem. This problem involves the exponential shrinking of gradients as they are propagated back through the network. As the gradient of the loss function gets backpropagated to the earlier layers, the gradients can become very small—essentially, they "vanish." This makes these earlier layers very hard to train, and can lead to the network effectively becoming shallower than intended.
This problem is particularly pronounced in deep networks because of their depth—the more layers a gradient has to be backpropagated through, the more it can potentially vanish.
The other options, such as Insufficient hardware, Over-regularization, and Lack of data, can also pose challenges in training deep convolutional neural networks, but they are not the primary challenge.
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