NPTEL Deep Learning – IIT Ropar Week 10 Assignment Answers 2025

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NPTEL Deep Learning – IIT Ropar Week 10 Assignment Answers 2025

1. Consider an input image of size 1000 × 1000 × 7 where 7 refers to the number of channels (Such images do exist!). Suppose we want to apply a convolution operation on the entire image by sliding a kernel of size 1×1×d. What should be the depth d of the kernel?

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2. For the same input image in Q1, suppose that we apply the following kernels of differing sizes.

K1 :5×5
K2 :7×7
K3 :25×25
K4 :41×41
K5 :51×51

Assume that stride s=1 and no zero padding. Among all these kernels which one shrinks the output dimensions the most?

  • K1
  • K2
  • K3
  • K4
  • K5
Answer :-

3. Which of the following is a technique used to fool CNNs in Deep Learning?

  • Transfer learning
  • Dropout
  • Batch normalization
  • Adversarial examples
Answer :-

4. What is the motivation behind using multiple filters in one Convolution layer?

  • Reduced complexity of the network
  • Reduced size of the convolved image
  • Insufficient information
  • Each filter captures some feature of the image separately
Answer :-

5. Which of the following statements about CNN is (are) true?

  • CNN is a feed-forward network
  • Weight sharing helps CNN layers to reduce the number of parameters
  • CNN is suitable only for natural images
  • The shape of the input to the CNN network should be square
Answer :-

6. Consider an input image of size 100 × 100 × 1. Suppose that we used kernel of size 3×3, zero padding P=1 and stride value S=3. What will be the output dimension?

  • 100 × 100 × 1
  • 3 × 3 × 1
  • 34 × 34 × 1
  • 97 × 97 × 1
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7. Consider an input image of size 100 × 100 × 3. Suppose that we use 8 kernels (filters) each of size 1 × 1, zero padding P=1 and stride value S = 2. How many parameters are there? (assume no bias terms)

  • 3
  • 24
  • 10
  • 8
  • 100
Answer :-

8. What is the purpose of guided backpropagation in CNNs?

  • To train the CNN to improve its accuracy on a given task.
  • To reduce the size of the input images in order to speed up computation.
  • To visualize which pixels in an image are most important for a particular class prediction.
  • None of the above.
Answer :-

9. Which of the following statements is true regarding the occlusion experiment in a CNN?

  • It is a technique used to prevent overfitting in deep learning models.
  • It is used to increase the number of filters in a convolutional layer.
  • It is used to determine the importance of each feature map in the output of the network.
  • It involves masking a portion of the input image with a patch of zeroes.
Answer :-

10. Which of the following architectures has the highest no of layers?

  • AlexNet
  • GoogleNet
  • ResNet
  • VGG
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