CS 7643 Quiz 2 Questions and Answers 2025 Update with complete solution
Convolution Features - (answer)edges
colors
textures
motifs (corners, shapes)
Receptive field - (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 - (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 - (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 - (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 - (answer)(K1 x K2 + 1) x M
where M is the number of features
, CS 7643 Quiz 2 Questions and Answers 2025 Update with complete solution
Relationship between convolution and cross-correlation - (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 - (answer)When the kernel is fully on the image. (No padding)
Output size of the vanilla convolution,
given H, W, K1, K2 - (answer)(H - K1 + 1) x (W - K2 + 1)
How to add padding - (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 - (answer)Number of pixels moving forward when parsing the patch through
images.
Loss of information
Used for dimensionality reduction
Effect of channels on output size - (answer)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 - (answer)Each channel might have its own weights with respect to the
same kernel.
M x (Ch x K1 x K2 + 1)
Effect of multiple kernels (feature extraction) on output size. - (answer)The kernel size should be equal
(K1 x K2) for each kernel within the layer. The output size:
(H - K1 + 1) x (W - K2 + 1) x Number of Kernels
Convolution Features - (answer)edges
colors
textures
motifs (corners, shapes)
Receptive field - (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 - (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 - (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 - (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 - (answer)(K1 x K2 + 1) x M
where M is the number of features
, CS 7643 Quiz 2 Questions and Answers 2025 Update with complete solution
Relationship between convolution and cross-correlation - (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 - (answer)When the kernel is fully on the image. (No padding)
Output size of the vanilla convolution,
given H, W, K1, K2 - (answer)(H - K1 + 1) x (W - K2 + 1)
How to add padding - (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 - (answer)Number of pixels moving forward when parsing the patch through
images.
Loss of information
Used for dimensionality reduction
Effect of channels on output size - (answer)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 - (answer)Each channel might have its own weights with respect to the
same kernel.
M x (Ch x K1 x K2 + 1)
Effect of multiple kernels (feature extraction) on output size. - (answer)The kernel size should be equal
(K1 x K2) for each kernel within the layer. The output size:
(H - K1 + 1) x (W - K2 + 1) x Number of Kernels
