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Deep Learning · End Term · 31 Aug 2025 · May 2025 term · Set QDD3

Question 2: How does unsupervised layerwise pretraining help in allev…

Question 2

+2 marksOne correct option

How does unsupervised layerwise pretraining help in alleviating the vanishing gradient problem?

  1. A

    It allows the network to learn a better representation of the data in each layer, which leads to better- initialized weights for subsequent supervised training.

  2. B

    It adds skip connections to the network, which are then removed before the supervised training.

  3. C

    It replaces the sigmoid functions with ReLU functions during the pretraining phase.

  4. D

    It regularizes the network’s weights, making them smaller and less likely to cause the gradients to explode.

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Correct answer

  • A

    It allows the network to learn a better representation of the data in each layer, which leads to better- initialized weights for subsequent supervised training.

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Question 2 of 19 in the IIT Madras BS Deep Learning (Deep Learning) End Term paper sat on 31 Aug 2025, in the May 2025 term (IIT M IMPROVEMENT FN EXAM QIC1 31 Aug 2025). It carries 2 marks.

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