I am confused about how neural networks weigh different features or inputs.
Consider this example. I have 3 features/inputs: an image, a dollar amount, and a rating. However, since one feature is an image, I need to represent it with very high dimensionality, for example with $128 \times 128 = 16384$ 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 $16384$ '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?