CS7643-QUIZ2 EXAM 2025/2026
QUESTIONS AND ANSWERS 100% PASS
Convolution Features - ANS edges
colors
textures
motifs (corners, shapes)
Receptive field - ANS A region of an image (image patch) from which the node receives input.
Usually denoted by a K1 x K2 matrix.
Convolution vs Cross-correlation - ANS Convolution: flip the kernel (rotate 180) and take the
dot product with image patch
Cross-correlation: do not flip the kernel to take the dot product with image patch
Advantage of using image patch - ANS 1./ Reduces the input parameters to
K1 x K2 + 1 (bias)
for each output node. Thus, the total number of input parameters:
N x (K1 + K2 + 1)
2./ Explicitly maintains spatial information
Weight sharing - ANS The weights will represent what types of features we will extract. The
weights (W) will be the same for each output node with respect to a specific kernel, regardless
of the specific image patch we are looking at.
1 @COPYRIGHT 2025/2026 ALLRIGHTS RESERVED
, The total number of input parameters:
K1 x K2 + 1
Input parameters with multiple feature extractions - ANS (K1 x K2 + 1) x M
where M is the number of features
Relationship between convolution and cross-correlation - ANS Duality: If cross-correlation is
the forward pass (which is the easier operation), the convolution operation is going to be the
backward pass to calculate gradients (vice versa)
Valid convolution - ANS When the kernel is fully on the image. (No padding)
Output size of the vanilla convolution,
given H, W, K1, K2 - ANS (H - K1 + 1) x (W - K2 + 1)
How to add padding - ANS Increases the size of the image with P in both directions (top &
bottom, left & right)
--> (H + 2P) x (W + 2P)
Can be filled with zeros or mirror the image
Stride and its consequences - ANS Number of pixels moving forward when parsing the patch
through images.
Loss of information
Used for dimensionality reduction
Effect of channels on output size - ANS It doesn't have effect on the output size: we perform
the dot product for each channels and summing them up.
Effect of channels on parameters - ANS Each channel might have its own weights with
respect to the same kernel.
2 @COPYRIGHT 2025/2026 ALLRIGHTS RESERVED
QUESTIONS AND ANSWERS 100% PASS
Convolution Features - ANS edges
colors
textures
motifs (corners, shapes)
Receptive field - ANS A region of an image (image patch) from which the node receives input.
Usually denoted by a K1 x K2 matrix.
Convolution vs Cross-correlation - ANS Convolution: flip the kernel (rotate 180) and take the
dot product with image patch
Cross-correlation: do not flip the kernel to take the dot product with image patch
Advantage of using image patch - ANS 1./ Reduces the input parameters to
K1 x K2 + 1 (bias)
for each output node. Thus, the total number of input parameters:
N x (K1 + K2 + 1)
2./ Explicitly maintains spatial information
Weight sharing - ANS The weights will represent what types of features we will extract. The
weights (W) will be the same for each output node with respect to a specific kernel, regardless
of the specific image patch we are looking at.
1 @COPYRIGHT 2025/2026 ALLRIGHTS RESERVED
, The total number of input parameters:
K1 x K2 + 1
Input parameters with multiple feature extractions - ANS (K1 x K2 + 1) x M
where M is the number of features
Relationship between convolution and cross-correlation - ANS Duality: If cross-correlation is
the forward pass (which is the easier operation), the convolution operation is going to be the
backward pass to calculate gradients (vice versa)
Valid convolution - ANS When the kernel is fully on the image. (No padding)
Output size of the vanilla convolution,
given H, W, K1, K2 - ANS (H - K1 + 1) x (W - K2 + 1)
How to add padding - ANS Increases the size of the image with P in both directions (top &
bottom, left & right)
--> (H + 2P) x (W + 2P)
Can be filled with zeros or mirror the image
Stride and its consequences - ANS Number of pixels moving forward when parsing the patch
through images.
Loss of information
Used for dimensionality reduction
Effect of channels on output size - ANS It doesn't have effect on the output size: we perform
the dot product for each channels and summing them up.
Effect of channels on parameters - ANS Each channel might have its own weights with
respect to the same kernel.
2 @COPYRIGHT 2025/2026 ALLRIGHTS RESERVED
