Guide-linked quiz · Medium Level

Dilated and Atrous Convolutions Quiz

Check dilation spacing, receptive fields, segmentation, WaveNet, gridding, and parallel multi-scale context in atrous convolutions.

Question 1 of 8

What does a dilation rate greater than 1 do to a convolution kernel?

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  1. What does a dilation rate greater than 1 do to a convolution kernel?
    • It inserts gaps between the kernel weights so they cover a wider area
    • It adds more learnable weights to the kernel
    • It shrinks the kernel to a single point
    • It converts the kernel into a pooling operation
  2. A 3x3 kernel with a dilation rate of 2 effectively spans how large a region?
    • 3x3
    • 5x5
    • 7x7
    • 9x9
  3. Why are dilated convolutions especially useful for semantic segmentation?
    • They reduce the image to a single label
    • They eliminate the need for any training data
    • They enlarge the receptive field while keeping the feature map resolution high
    • They only work on grayscale images
  4. What does the term atrous originate from?
    • A Latin word for layer
    • An acronym for a training method
    • The name of its inventor
    • The French a trous, meaning with holes
  5. In WaveNet, why are stacked dilated convolutions valuable?
    • They generate images instead of audio
    • They capture very long-range dependencies in the audio waveform efficiently
    • They remove all silence from recordings
    • They convert audio into text directly
  6. What problem can arise if you stack dilated convolutions with a poorly chosen single rate?
    • The network becomes too small to train
    • The image is permanently rotated
    • Gridding artifacts, where some input positions are never sampled
    • All weights become zero
  7. How does Atrous Spatial Pyramid Pooling combine multiple scales in DeepLab?
    • It runs several dilation rates in parallel and fuses their outputs
    • It chooses one fixed dilation rate and discards the rest
    • It converts the image into a single global class before convolution
    • It replaces every convolution with a larger dense kernel
  8. For one input channel and one output channel, how many kernel weights does a dilated 3×3 filter contain, excluding a bias?
    • 25, because dilation 2 spans a 5×5 region
    • 5, one for each position along the expanded width
    • 18, because dilation 2 doubles the number of weights
    • 9, because dilation changes spacing rather than the 3×3 weight count

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