CNN Classic CNN Architectures 4

Question 1

Which classic CNN architecture introduced Local Response Normalization (LRN)?

Accepted Answer

AlexNet

Answer

AlexNet introduced Local Response Normalization, which normalizes across adjacent feature maps to encourage competition between neurons — though later research found it rarely beneficial.

Question 2

How many parameters does GoogLeNet have compared to AlexNet?

Accepted Answer

GoogLeNet has roughly 12x fewer parameters

Answer

AlexNet has ~60M parameters while GoogLeNet has only ~5M, making GoogLeNet roughly 12x more parameter-efficient due to its Inception modules.

Question 3

What pooling strategy does GoogLeNet use at the end of the network instead of fully connected layers?

Accepted Answer

Average pooling

Answer

GoogLeNet uses global average pooling before the final classifier, drastically reducing parameters compared to flattening into large fully connected layers.

Question 4

What is the approximate top-5 error rate AlexNet achieved on ImageNet ILSVRC 2012?

Accepted Answer

15.3%

Answer

AlexNet achieved a top-5 error of 15.3%, which was dramatically better than the runner-up's 26.2%, demonstrating deep CNN superiority.

Question 5

Which VGGNet variant performs best on ImageNet while balancing accuracy and parameter count?

Accepted Answer

VGG-16

Answer

VGG-16 is the most commonly used variant, offering a strong balance between accuracy and parameter efficiency compared to VGG-19 with only marginal gains.

CNN - Convolutional Neural Networks Practice Test

CNN - Convolutional Neural Networks Practice Test

CNN Classic CNN Architectures 4