Question 132 NAT · 2.0 marks
Based on the above data, answer the given subquestions.
Number of Parameters: ______________
A published solution is not available for this question yet.
Deep Learning for Computer Vision · Quiz 2 · Sep 2024
Deep Learning for Computer Vision · Quiz 2 · Sep 2024
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Question 133 NAT · 2.0 marks
Based on the above data, answer the given subquestions.
Computational Cost: ______________
A published solution is not available for this question yet.
Question 134 NAT · 2.0 marks
Calculate the number of parameters and computational cost, but make use of **Depthwise**
**Seperable convolution** and **pointwise convolution** instead of standard convolution.
Based on the above data, answer the given subquestions.
Number of parameters for depthwise convolution: ______________
A published solution is not available for this question yet.
Question 135 NAT · 2.0 marks
Calculate the number of parameters and computational cost, but make use of **Depthwise**
**Seperable convolution** and **pointwise convolution** instead of standard convolution.
Based on the above data, answer the given subquestions.
Computational Cost for depthwise convolution: ______________
A published solution is not available for this question yet.
Question 136 NAT · 2.0 marks
Calculate the number of parameters and computational cost, but make use of **Depthwise**
**Seperable convolution** and **pointwise convolution** instead of standard convolution.
Based on the above data, answer the given subquestions.
Number of parameters for pointwise convolution: ______________
A published solution is not available for this question yet.
Question 137 NAT · 2.0 marks
Calculate the number of parameters and computational cost, but make use of **Depthwise**
**Seperable convolution** and **pointwise convolution** instead of standard convolution.
Based on the above data, answer the given subquestions.
Computational cost for pointwise convolution: ______________
A published solution is not available for this question yet.
Question 138 MCQ · 2.0 marks
A published solution is not available for this question yet.
Question 139 MCQ · 2.0 marks
Which one of the following object detection networks uses an ROI pooling layer?
Fast R-CNN
R-CNN
YOLO
All of these
A published solution is not available for this question yet.
Question 140 MCQ · 2.0 marks
Consider an object detection system evaluated on a dataset consisting of 1000 images. The system
makes 1500 predictions across these images, and for each image, there are annotated ground
truth bounding boxes.
The system’s precision-recall curve is calculated, and the precision at different recall levels for one
of the classes is as follows:
Calculate the Average Precision (AP) for this class using the 11-point interpolation method, which
averages the precision values at recall levels {0.0, 0.1, 0.2, ..., 1.0}. The precision at recall 0.0 can be
assumed to be 1.0.
Additionally, the system’s AP values for the other two classes are as follows:
- AP for class 2: 0.78
- AP for class 3: 0.72
Based on these AP values, what is the mean Average Precision (mAP) across all three classes?
0.70
0.69
0.73
0.76
A published solution is not available for this question yet.
Question 141 MCQ · 2.0 marks
1→iv, 2→ii, 3→v, 4→i
1→iv, 2→iii, 3→v, 4→ii
1→iv, 2→v, 3→ii, 4→iii
1→iii, 2→v, 3→i, 4→ii
A published solution is not available for this question yet.
Question 142 MCQ · 2.0 marks
Which one of the following statements is False?
In a max pooling layer, the unit that gets selected (maximum entry) in forward
propagation gets all the gradient during backpropagation.
Residual blocks are used in ResNets to avoid the vanishing gradient problem.
Feature maps of initial layers of a CNN capture more abstract concepts than
the feature maps of final layers.
Dilated convolution increases the receptive field size when compared to the
standard convolution operator.
A published solution is not available for this question yet.
Question 143 MCQ · 2.0 marks
Sigmoid of alignment scores
Softmax of alignment scores
ReLU of alignment scores
None of these
A published solution is not available for this question yet.
Question 144 MCQ · 2.0 marks
Which one of the following statements is **false?**
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.
None of these
A published solution is not available for this question yet.
Question 145 MCQ · 2.0 marks
Consider two 12×12 bounding boxes(one on the upper left and one of the lower right) in an
images with an overlapping region of 8 × 8. The Intersection over Union (IoU) between the two
boxes is (choose the closest value):
21%
25%
28%
37%
A published solution is not available for this question yet.
Question 146 MCQ · 2.0 marks
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.
None of these
A published solution is not available for this question yet.
Question 147 MCQ · 2.0 marks
1→ iv, 2→ iii, 3→ ii, 4 → i
1→ ii, 2→ iii, 3 → i, 4 →iv
1→ iv, 2→ iii, 3→ i, 4 → ii
1→ ii, 2→ iv, 3 → iii, 4→ i
A published solution is not available for this question yet.
Question 148 MCQ · 2.0 marks
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
A published solution is not available for this question yet.
Question 149 MCQ · 2.0 marks
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
A published solution is not available for this question yet.
Question 150 MSQ · 2.0 marks
Which of the following are **true:**
A published solution is not available for this question yet.
Question 151 MSQ · 2.0 marks
Which of the following is true regarding Hard Attention and Soft Attention?
Soft Attention is smooth and differentiable
Variance reduction techniques are used to train Soft Attention models
Soft Attention is computationally cheaper than Hard Attention when the
source input is large
The inference (test time) overhead is low in Hard Attention when compared to
Soft Attention models
A published solution is not available for this question yet.
Question 152 NAT · 2.0 marks
Consider a Faster R-CNN object detection framework, which is being trained with a mini-batch size
of 2. The RPN produces 512 proposals, out of which 5 and 95 of them are foreground proposals,
corresponding to each image in the mini-batch. For a single mini-batch update, the bounding box
regression loss will be computed _____________ times.
A published solution is not available for this question yet.
Question 153 NAT · 2.0 marks
The RNN given below is used for classification:
Based on the above data, answer the given subquestions.
Number of weights in Weight Matrix U1 is: ______________
A published solution is not available for this question yet.
Question 154 NAT · 2.0 marks
The RNN given below is used for classification:
Based on the above data, answer the given subquestions.
Number of weights in Weight Matrix V1 is: ______________
A published solution is not available for this question yet.
Question 155 NAT · 2.0 marks
The RNN given below is used for classification:
Based on the above data, answer the given subquestions.
Number of weights in Weight Matrix U2 is: ______________
A published solution is not available for this question yet.
Question 156 NAT · 2.0 marks
The RNN given below is used for classification:
Based on the above data, answer the given subquestions.
Number of weights in Weight Matrix V2 is: ______________
A published solution is not available for this question yet.