I’m using a 2d CNN. My input size is a square image of 20x20 pixels and my desired output has size 400x3.
The most common thing I see is to introduce padding over the convolutional layers in order to reach the desired output size and let the nn train.
The output I get in this way is characterized by lots of zeros (sparse matrix) and obviously the performance is very low. So what else can I do? I tried to introduce more layers, each one characterized by smaller padding values but it doesn’t improve…
any hint will be really appreciated, thanks!