Q1. Which of the following is not a good evaluation metric for Multi-label classification?
Mean Average Precision at K
Hamming Score
Accuracy
Top k categorical accuracy
Answer: 3 Explanation: Accuracy is not a good evaluation metric for Multi-label classification. As we know in multi-label each example can be assigned to multiple classes so let’s say if the predicted output was [0, 0, 0, 0, 1, 1,0] and the correct output was [1, 1, 0, 0, 0, 0, 0], my accuracy would still be 3/6 but it should be 0 as it is not able to predict any of the classes correctly.
Q2. Which of the following are the hyperparameters for a Pooling layer?
filter size
stride
which type of Pooling to use (max or average)
All of the above
Answer: 4 Explanation: All of the above mentioned are the hyperparameters for a Pooling layer.
Q3. Images are an example of ________ data?
Structured
Unstructured
Answer: 2 Explanation: Structured data refers to the type of data where each feature has a well defined meaning and opposite is true for unstructured data. So, images are an example of unstructured data.
Q4. For image classification, MaxPooling tends to works better than average pooling?
Yes
No
Answer: 1 Explanation: Because in image classification our main aim is to identify whether a feature is present or not so MaxPooling tends to works better than average pooling.
Q5. What is Pointwise Convolution?
1×1 convolution
Strided Convolution
convolution followed by MaxPool
convolution followed by Dropout
Answer: 1 Explanation: According to the MobileNet paper, “After depthwise convolution, The pointwise convolution then applies a 1×1 convolution to combine the outputs of the depthwise convolution.”. Refer to Section 3.1 of this research paper to understand more.
Q6. What is a Region Proposal network?
a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position
a fully connected network that simultaneously predicts object bounds and objectness scores at each position
a fully convolutional network that predicts only the objectness scores at each position
a fully connected network that predicts only the object bounds at each position
Answer: 1 Explanation: According to the Faster R-CNN paper, Region Proposal network (RPN) is a fully convolutional network that takes an image(of any size) as input and outputs a set of rectangular object proposals, each with an objectness score. Refer to Section 3.1 of this research paper to understand more.
Q7. In MobileNetv2, the Depthwise Separable Convolutions are replaced by _________ ?
Normal Convolution
Strided Convolution
Bottleneck Residual Block (Inverted Residuals and Linear Bottleneck)
Residual Blocks
Answer: 3 Explanation: In MobileNetv2, the Depthwise Separable Convolutions are replaced by Bottleneck Residual Block (Inverted Residuals and Linear Bottleneck). Refer to Table 1 of this research paper to understand more.
Q8. Can we use Convolutional Neural Networks for image classification?
Yes
No
Answer: 1 Explanation: Generally, Convolutional Neural Networks are preferred for any image related tasks such as image classification, object detection etc.