I am confused as to how neural networks consider the different features by that have access to at the input layer.
Consider this example: I have three features: an image, a dollar amount, and a rating. However, since the one feature is an image, I need to represent it with very high dimensionality, for example with 128x128=16,384 pixel values. (I am just using 'image' as an example, my question holds for any feature that needs high dimensional representation: word counts, one-hot encodings, etc.)
Will the 16,384 'features' representing the image completely overwhelm the other 2 features that are the dollar amount and rating? Ideally, I would think the network would consider each of the three true features relatively equally. Would this issue naturally resolve itself in the training process? Would training become much more difficult of a task?