NPTEL Deep Learning for Computer Vision Week 8 Assignment Answers 2024

Sanket
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NPTEL Deep Learning for Computer Vision Week 8 Assignment Answers 2024

1. Match the following:

W8A8Q1
  • 1 → iii, 2 → iv, 3 → ii, 4 → i
  • 1 → iv, 2 → i, 3 → iii, 4 → ii
  • 1 → i, 2 → ii, 3 → iii, 4 → iv
  • 1 → i, 2 → iii, 3 → ii, 4 → iv
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2.

image 91
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3. Which of the following statements are true? (Select all options that apply)

  • The number of learnable parameters in an RNN grows exponentially with input sequence length considered.
  • An image classification task with a batch size of 16 is a sequential learning problem.
  • In an RNN, the current hidden state ht not only depends on the previous hidden state ht−1 but implicitly depends on earlier hidden states also.
  • Generating cricket commentary for a corresponding video snippet is a sequence learning problem.
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4. Which one of the following statements is true?

  • Attention mechanisms cannot be applied to the bidirectional RNN model
  • An image captioning network cannot be trained end-to-end even though we are using 2 different modalities to train the network
  • One of the key components in the vanilla transformer are the recurrent connections that help them to deal with variable input length.
  • All of the above
  • None of the above
Answer :- 

5. Match the following ways of boosting image captioning techniques with attributes. Here, I =Image; A = Image Attributes; f(.) is the function applied on them.

W8A8Q2
  • 1→iii, 2→ii, 3→v, 4→iv,5→i
  • 1→iii, 2→iv, 3→i, 4→ii,5→v
  • 1→iii, 2→iv, 3→v, 4→ii,5→i
  • 1→i, 2→iii, 3→iv, 4→ii,5→v
Answer :- 

6. Which of the following statements are true? (Select all possible correct options)

  • Autoencoder can be equivalent to Principal Component Analysis (PCA) provided we make use of non-linear activation functions
  • When using global attention on temporal data, alignment weights are learnt for encoder hidden representations for all time steps
  • Positional encoding is an important component of the transformer architecture as it conveys information about order in a given sequence
  • It is not possible to generate different captions for the same image that have similar meaning but different tone/style
  • Autoencoders can not be used for data compression as its input and output dimensions are different
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7. Which of the following is true regarding Hard Attention and Soft Attention?

  • Hard Attention is smooth and differentiable
  • Variance reduction techniques are used to train Hard Attention models
  • Soft Attention is computationally cheaper than Hard Attention when the source input is large
  • All of the above
  • None of the above
Answer :- 

8. Match the following attention mechanisms to their corresponding alignment score functions:

W8A8Q8
  • 1→iii, 2→ii, 3→v, 4→iv,5→i
  • 1→iii, 2→iv, 3→i, 4→ii,5→v
  • 1→i, 2→iii, 3→iv, 4→ii,5→v
  • 1→v, 2→iv, 3→iii, 4→i,5→ii
Answer :- 

9. Which of the following statements is true (select all that apply):

  • The number of learnable parameters in an RNN grows exponentially with input sequence length considered
  • Long sentences give rise to the vanishing gradient problem
  • Electrocardiogram signal classification is a sequence learning problem
  • RNNs can have more than one hidden layer
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The RNN given below is used for classification:

W8A8Q10

The dimensions of the layers of RNN are as follows:

• Input X∈R132

• Hidden Layer 1 D1 ∈R256

• Hidden Layer 2 D2 ∈R128

• Number of classes:15

Note: Do not consider the bias term

10. Number of weights in Weight Matrix U1 is

Answer :- 

11. Number of weights in Weight Matrix V1 is

Answer :- 

12. Number of weights in Weight Matrix U2 is

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13. Number of weights in Weight Matrix V2 is

Answer :- 

14. Number of weights in Weight Matrix W is

Answer :- 

Consider an LSTM cell, and the data given below:

xt=3
ht−1=2
Wf =[0.1,0.2]
bf =0
Wi =[-0.1,-0.3]
bi =-1
Wo =[-1.2,-0.2]
bo =1.5
WC =[3,-1]
bC =0.5
Ct−1 =-0.5

Compute the following quantities (round upto 3 decimal places, refer formulas from lecture slides for computation). Note that we use scalars and vectors for ease of calculation here; in a realistic setup, this will be matrices and not scalars.

15. Forget Gate ft

Answer :- 

16. Input Gate it

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17. Output Gate it

Answer :- 

18. New cell content Ct

Answer :- 

19. Cell State Ct

Answer :- 

20. Hidden state ht

Answer :- 

Consider a GRU cell, and the following data:

  • xt =1.5
  • ht−1 =-0.5
  • Wz =[1.1,1.2]
  • Wr =[-1.1,-1.3]
  • W=[-1,-0.5]

Compute the following quantities (round upto 3 decimal places, bias values can be considered zero, refer formulas from lecture slides for computation). Note that we use scalars and vectors for ease of calculation here; in a realistic setup, this will be matrices and not scalars.

21. Update Gate zt

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22. Reset Gate rt

Answer :- 

23. New hidden state content ht

Answer :- 

24. hidden state ht

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