Hi Mentor,
@thearkamitra
@arosacastillo
@AmmarMohanna
@XpRienzo
@reinoudbosch
@chrismoroney39
@paulinpaloalto
We are unable to understand the concept in the lecture Convolutional Implementation of Sliding Window. Please help us on this, we are struck into this video.
Statement 1 at 7:45 minute, It turns out that this blue 1 by 1 by 4 subset gives
you the result of running in the upper left hand corner 14 by 14 image. This upper right 1 by 1 by 4 volume gives you the upper right result. The lower left gives you the results of implementing the convnet on the lower left 14 by 14 region. And the lower right 1 by 1 by 4 volume gives you the same result as running the convnet on the lower right 14 by 14 medium
Doubt in statement 1 : We are unable to get intuition behind this statement. How it reduced four times convnet forward propagation computation to one forward propagation. Because above statement telling like convnet is running on the left , right , top , bottom corner of the 14 by 14 image then this implies convnet applied four times right ie convnet applying across all four different positions but why so one time forward propagation saying in the lecture?
Statement 2 at 8:53 : Instead, it combines all four into one form of computation and shares a lot of the computation in the regions of image that are common
Doubt 2: Can u please explain with example of this statement (shares a lot of the computation in the regions of image that are common)…how computation shared ?
Statement 3 at 9:54 Because of the max pooling up too that this corresponds to running your neural network with a stride of two on the original image. what does it mean sir ?