High School Level Neural Networks Quiz

In fully-connected layers, how does input pass through activation units?

• As individual pixels

• As a whole entity

• Through a floating window

• By specific pixel clusters

Why are fully-connected layers often not the first choice for computational efficiency?

• They are too simple

• They require less memory

• They are computationally intensive

• They are less prone to overfitting

What is a disadvantage of fully-connected layers regarding image shape?

• They are independent of image shape

• They improve with varying image shapes

• They become dependent on the shape of train images

• They maintain a fixed image shape

How do fully-connected layers compare to single convolution operations in terms of parameters?

• Fully-connected layers have fewer parameters

• Single convolution operations increase parameters

• Fully-connected layers have a larger number of parameters

• Both have an equal number of parameters

What can a Convolutional Neural Network (ConvNet) assign to various objects in an image?

• Random values

• Learnable weights and biases

• Fixed pixel colors

• Output predictions directly

How does a ConvNet capture spatial and temporal dependencies in an image?

• By ignoring filters

• Through random sampling

• By applying relevant filters

• Using only fully-connected layers

Which of the following is a component of a ConvNet architecture?

• Output layer only

• Input, Convolutional Layers, Pooling Layers, Fully-Connected Layers

• Activation units only

• Image processing software

What happens when images reach higher dimensions, such as 8K images, in terms of computation?

• It becomes less intensive

• It remains unchanged

• It becomes computationally-intensive

• It speeds up processing

What can ConvNet do to image dimensions without losing critical features?

• Increase them

• Maintain them

• Reduce them

• Distort them

What are common filter (kernel) sizes for convolutional layers?

• 1x1 or 2x2

• 3x3 or 5x5

• 7x7 or 9x9

• 10x10 or larger