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WorksheetsCNN Layer
Total questions: 10
Worksheet time: 2mins
1. What is the primary function of a convolutional layer in a CNN?
a) Reduce the number of parameters
b) Detect spatial patterns and features
c) Convert the image to grayscale
d) Flatten the image into a vector
2. Which CNN layer helps reduce spatial dimensions while preserving key features?
a) Convolutional layer
b) Fully connected layer
c) Pooling layer
d) Batch normalization layer
3. In an RGB image, how many channels does the input have?
a) 1
b) 2
c) 3
d) 4
4) Which CNN layer typically appears at the end of the architecture for classification tasks?
a) Convolutional layer
b) Pooling layer
c) Fully connected layer
d) Dropout layer
5) . How does transposed convolution differ from standard convolution?
a) It increases the spatial dimensions instead of decreasing them
b) It replaces convolutional layers with fully connected layers
c) It does not use filters or kernels
d) It operates only on grayscale images
6) Which application commonly uses transposed convolutional networks?
a) Image classification
b) Image segmentation and generation
c) Sentiment analysis
d) Reinforcement learning
7) In which of the following applications is CNN-based color processing commonly used?
a) Image segmentation and colorization
b) Sentiment analysis and speech recognition
c) Reinforcement learning and game AI
d) Data compression and encryption
8. What is the main advantage of using dilated convolutions in neural networks?
a) Reducing overfitting
b) Increasing receptive field without increasing parameters
c) Reducing computational cost
d) Improving pooling efficiency
9. In a dilated convolution, what does the dilation rate control?
a) The number of filters used
b) The spacing between kernel elements
c) The stride of the convolution
d) The activation function used
10) How do dilated convolutions compare to standard convolutions in terms of feature extraction?
a) They extract features from a larger area without increasing the number of parameters
b) They are always slower than standard convolutions
c) They do not perform well with high-resolution images
d) They require more memory due to larger kernel size.
