Answer: 3 Explanation: The values in a filter/mask are called as either coefficients or weights.
Q2. Which of the following networks uses the idea of Depthwise Separable Convolutions?
AlexNet
MobileNet
ResNet
VGG16
Answer: 2 Explanation: As mentioned in the MobileNet paper, MobileNets are based on a streamlined architecture that uses depthwise separable convolutions to build light weight deep neural networks that work even in low compute environment, such as a mobile phones. Refer to this research paper to understand more.
Q3. What is the output of a Region Proposal Network (RPN) at each sliding window location if we have k anchor boxes?
2k scores and 4k bounding box coordinates
4k scores and 2k bounding box coordinates
k scores and 4k bounding box coordinates
4k scores and 4k bounding box coordinates
Answer: 1 Explanation: In a Region Proposal Network (RPN), for k anchor boxes we get 2k scores (that estimate probability of object or not) and 4k bounding box coordinates corresponding to each sliding window location. Refer to Figure 3 of this research paper to understand more.
Q4. Which of the following networks uses Skip-connections?
DenseNet
ResNet
U-Net
All of the above
Answer: 4 Explanation: All of the above mentioned networks uses Skip-connections.
Q5. For binary classification, we generally use ________ activation function in the output layer?
Tanh
ReLU
Sigmoid
Leaky ReLU
Answer: 3 Explanation: For binary classification, we want the output (y) to be either 0 or 1. Because sigmoid outputs the P(y=1|x) and has value between 0 and 1, so it is appropriate for binary classification.
Q6. In ResNet’s Skip-connection, the output from the previous layer is ________ to the layer ahead?
added
concatenated
convoluted
multiplied
Answer: 1 Explanation: In ResNet’s Skip-connection, the output from the previous layer is added to the layer ahead. Refer to the Figure 2 of this research paper to understand more.
Q7. In Fast R-CNN, we extract feature maps from the input image only once as compared to R-CNN where we extract feature maps from each region proposal separately?
True
False
Answer: 1 Explanation: Earlier in R-CNN we were extracting features from each region proposals separately using a CNN and this was very time consuming. So, to counter this, in Fast R-CNN we extract feature maps from the input image only once and then project the region proposals onto this feature map. This saves a lot of time. Refer to this link to understand more.
Q8. For Multiclass classification, we generally use ________ activation function in the output layer?
Tanh
ReLU
Sigmoid
Softmax
Answer: 4 Explanation: For Multiclass classification, we generally use softmax activation function in the output layer. Refer to this beautiful explanation by Andrew Ng to understand more.