Quiz Space

Deep Learning for Computer Vision · Quiz 2 · 23 Nov 2025 · September 2025 term

Question 6: Consider the following statements P and Q regarding AlexN…

Question 6

+2 marksOne correct option

Consider the following statements P and Q regarding AlexNet and choose the correct option: (P) In AlexNet, a trainable Local Response Normalization Layers were introduced to emulate the competitive nature of real neurons, where highly active neurons suppress the activity of neighboring neurons, creating competition among different kernel outputs.
(Q) In AlexNet, a total of 8 Convolutional layers contain only about 6-7% of the total parameters hence account for the least computation.

  1. A

    Only statement P is true

  2. B

    Only statement Q is true

  3. C

    Both statements are true

  4. D

    None of the statements is true

Show answer

Correct answer

  • D

    None of the statements is true

See the whole paperTake it as a mock test

Question 6 of 51 in the IIT Madras BS Deep Learning for Computer Vision (Deep Learning for Computer Vision) Quiz 2 paper sat on 23 Nov 2025, in the September 2025 term (IIT M DEGREE AN EXAM QDB2 23 Nov 2025 NEW). It carries 2 marks.

More questions from this paper

  1. Q1Figure question
  2. Q2Which of the following architectures introduced the ROI pooling layer for extracting fixed-size feature maps from varia…
  3. Q3Which object detection method first integrated a Region Proposal Network (RPN) to generate region proposals within the …
  4. Q4Which architecture performs object detection as a single regression problem from image pixels to bounding boxes and cla…
  5. Q5Figure question
  6. Q7Consider two bounding boxes in an image:\ Box A: top-left at (0, 0), bottom-right at (12, 12) - Box B: top-left at (4, …
  7. Q8Which one of the following statements is True?
  8. Q9Match the following computer vision tasks to situations: | 1) Instance Segmentation | i) There are cars in these pixels…
  9. Q10Match the following: | 1) One-to-many RNN architecture | i) Sentiment analysis of Netflix reviews | |---|---| | 2) Many…
  10. Q11Figure question
  11. Q12Which of the following techniques help mitigate the vanishing gradient problem in recurrent neural networks?\ Gradient …
  12. Q13Which of the following techniques help mitigate the exploding gradient problem in recurrent neural networks?\ Gradient …
  13. Q14Which one of the following statements is false? (Pick the most appropriate one.)
  14. Q15Figure question
  15. Q16Which of the following is true regarding Hard Attention and Soft Attention?
  16. Q17Which of the following are False:
  17. Q18Which techniques help mitigate exploding/vanishing gradients in RNNs? (Select all that apply.)
  18. Q19Based on the above data, answer the given subquestions.
  19. Q20Based on the above data, answer the given subquestions.
  20. Q21Based on the above data, answer the given subquestions.
  21. Q22Based on the above data, answer the given subquestions.
  22. Q23Based on the above data, answer the given subquestions.
  23. Q24Based on the above data, answer the given subquestions.
  24. Q25Number of Parameters:_
  25. Q26Computational Cost:
  26. Q27Number of parameters for depthwise seperable convolution:
  27. Q28Computational Cost for depthwise seperable convolution:
  28. Q29Number of parameters for pointwise convolution:_
  29. Q30Computational cost for pointwise convolution:
  30. Q31Number of weights in Weight Matrix U1 is:_
  31. Q32Number of weights in Weight Matrix V1 is:_
  32. Q33Number of weights in Weight Matrix U2 is:_
  33. Q34Number of weights in Weight Matrix V2 is:_
  34. Q35Number of weights in Weight Matrix W is:_
  35. Q36You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  36. Q37You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  37. Q38You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  38. Q39You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  39. Q40You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  40. Q41You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  41. Q42You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  42. Q43You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  43. Q44You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  44. Q45You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  45. Q46You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  46. Q47You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  47. Q48You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  48. Q49You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  49. Q50You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…
  50. Q51You are given the encoder hidden states h_1, h_2, h_3 \in \mathbb{R}^2 and the decoder hidden state s_2 \in \mathbb{R}^…