Shouldn’t doubling the length and width of a 3 channel input image increase the number of activations just before the fully connected layer by a factor of 12 (2x2x3channels)?
How does the network use the weights it has trained when the input size increases? I can think of a couple of possibilities (all of which I don’t think are correct):
- Changing the stride of the convolutional filters - This will not work for resizing by every factor.
- Resizing the input image - This defeats the purpose of using larger images.
- Adding an untrained layer with the appropriate number of inputsand outputs before the fully connected layer - This would render the previous training useless.
It was difficult to articulate the question so let me know if I need to clarify it. Thanks!