In CNN, why do we increase the number of filters in deeper Convolution layers for complex images? I have been doing this online course Introduction to TensorFlow for AI, ML and DL. Here in one part, they were showing a CNN model for classifying human and horses. In this model, the first Conv2D layer had 16 filters, followed by two more Conv2D layers with 32 and 64 filters respectively. I am not sure how the number of filters is correlated with the deeper convolution layers.
For this you need to understand what filters actually do.
Every layer of filters is there to capture patterns. For example, the first layer of filters captures patterns like edges, corners, dots etc. Subsequent layers combine those patterns to make bigger patterns (like combining edges to make squares, circles, etc.).
Now as we move forward in the layers, the patterns get more complex; hence there are larger combinations of patterns to capture. That's why we increase the filter size in subsequent layers to capture as many combinations as possible.