CS7643-Quiz 2 Questions with Correct Answers 100% Verified By Experts|2025/2026 Latest
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Convolution Features edges
colors
textures
motifs (corners, shapes)
Receptive field 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 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 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 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 (K1 x K2 + 1) x M
where M is the number of features
, Relationship between convolution and cross-correlation 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 When the kernel is fully on the image. (No padding)
Output size of the vanilla convolution,
given H, W, K1, K2 (H - K1 + 1) x (W - K2 + 1)
How to add padding 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 Number of pixels moving forward when parsing the patch
through images.
Loss of information
Used for dimensionality reduction
Effect of channels on output size 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 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. The kernel size should be equal
(K1 x K2) for each kernel within the layer. The output size:
Update
Convolution Features edges
colors
textures
motifs (corners, shapes)
Receptive field 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 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 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 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 (K1 x K2 + 1) x M
where M is the number of features
, Relationship between convolution and cross-correlation 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 When the kernel is fully on the image. (No padding)
Output size of the vanilla convolution,
given H, W, K1, K2 (H - K1 + 1) x (W - K2 + 1)
How to add padding 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 Number of pixels moving forward when parsing the patch
through images.
Loss of information
Used for dimensionality reduction
Effect of channels on output size 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 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. The kernel size should be equal
(K1 x K2) for each kernel within the layer. The output size:
