CS7643-Quiz2 questions and answers 2026\2027
A+ Grade
Convolution Features
- correct answer edges
colors
textures
motifs (corners, shapes)
Receptive field
- correct answer 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
- correct answer 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
- correct answer 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
- correct answer 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.
The total number of input parameters:
K1 x K2 + 1
Input parameters with multiple feature extractions
- correct answer (K1 x K2 + 1) x M
where M is the number of features
Relationship between convolution and cross-correlation
- correct answer 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
- correct answer When the kernel is fully on the image. (No padding)
Output size of the vanilla convolution,
given H, W, K1, K2
- correct answer (H - K1 + 1) x (W - K2 + 1)
How to add padding
- correct answer 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
- correct answer Number of pixels moving forward when parsing the patch through images.
Loss of information
Used for dimensionality reduction
A+ Grade
Convolution Features
- correct answer edges
colors
textures
motifs (corners, shapes)
Receptive field
- correct answer 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
- correct answer 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
- correct answer 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
- correct answer 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.
The total number of input parameters:
K1 x K2 + 1
Input parameters with multiple feature extractions
- correct answer (K1 x K2 + 1) x M
where M is the number of features
Relationship between convolution and cross-correlation
- correct answer 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
- correct answer When the kernel is fully on the image. (No padding)
Output size of the vanilla convolution,
given H, W, K1, K2
- correct answer (H - K1 + 1) x (W - K2 + 1)
How to add padding
- correct answer 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
- correct answer Number of pixels moving forward when parsing the patch through images.
Loss of information
Used for dimensionality reduction
