Question 14
Consider an input image of shape 10×10 with 3 channels of R-G-B, is processed using one of the following neural architectures:
• Fully-connected layer (FC) with 2 neurons (Note: The input volume is “flattened” into a column vector before being fed into the layer.)
• Convolutional layer (CONV) with three filters of size 2 × 2, a stride of 2 is used, and no padding is applied.
Based on the above data answer the given subquestions.
What is the difference in the number of trainable parameters between these two neural architectures?