Which of the following is a primary challenge in training very deep convolutional neural networks?Group of answer choicesVanishing gradientsOver-regularizationLack of dataInsufficient hardware
Question
Which of the following is a primary challenge in training very deep convolutional neural networks?Group of answer choicesVanishing gradientsOver-regularizationLack of dataInsufficient hardware
Solution
The primary challenge in training very deep convolutional neural networks is Vanishing gradients.
Here's why:
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As we train a deep neural network, we use a method called backpropagation to adjust the weights of the network based on the error of the output.
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During backpropagation, gradients of the loss function are calculated with respect to these weights.
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However, with each layer we go back in the network, these gradients get smaller and smaller, leading to a very small change in the weights. This is known as the vanishing gradient problem.
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As a result, the weights of the initial layers (the ones closer to the input data) barely get updated and the network can't learn complex patterns effectively.
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This problem becomes more pronounced as the network becomes deeper, hence it's a primary challenge in training very deep convolutional neural networks.
The other options like Over-regularization, Lack of data, and Insufficient hardware can also pose challenges in training deep networks, but they are not as fundamental and prevalent as the vanishing gradient problem.
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