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Deep Learning Practice · End Term · 13 Apr 2025 · January 2025 term

Question 17: In deep convolutional neural networks, 1 x 1 convolution…

Question 17

+5 marksOne or more correct options

In deep convolutional neural networks, 1 x 1 convolutions play a significant role in optimizing computation and feature extraction. Which of the following statements correctly describe their role in modern CNN architectures?

Select all that apply.

  1. A

    1 x 1 convolutions can be used to perform dimensionality reduction by reducing the number of channels before applying computationally expensive convolutions.

  2. B

    They introduce non-linearity into the network, even when used without activation functions.

  3. C

    1 x 1 convolutions enable cross-channel interactions by linearly combining feature maps from different channels.

  4. D

    In ResNet architectures, 1 x 1 convolutions are used in bottleneck blocks to match the dimensions when applying skip connections.

  5. E

    When applied after a max pooling layer, 1 x 1 convolutions help recover lost spatial information by increasing the resolution of the feature maps.

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Correct answers

  • A

    1 x 1 convolutions can be used to perform dimensionality reduction by reducing the number of channels before applying computationally expensive convolutions.

  • C

    1 x 1 convolutions enable cross-channel interactions by linearly combining feature maps from different channels.

  • D

    In ResNet architectures, 1 x 1 convolutions are used in bottleneck blocks to match the dimensions when applying skip connections.

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Question 17 of 20 in the IIT Madras BS Deep Learning Practice (Deep Learning Practice) End Term paper sat on 13 Apr 2025, in the January 2025 term (IIT M FOUNDATION AN EXAM QDF3 13 Apr 2025). It carries 5 marks.

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