Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales
The study investigated deep networks designed to handle varying image scales by processing images at multiple scales simultaneously. While existing designs failed to generalize well, a new foveated architecture was proposed. This design processes larger image sections at decreasing resolutions, proving excellent generalization when resolution is sufficient and boundaries are avoided.