How do CNNs handle vanishing spatial information in very deep networks?

CNNs mitigate spatial information loss using skip connections, feature pyramids, and multi-scale feature fusion.

As CNN depth increases, repeated pooling and convolutions reduce spatial resolution, causing loss of fine details. Architectures like U-Net and FPN solve this by introducing skip connections from early layers to later layers, preserving high-resolution features. Multi-scale fusion also helps retain both coarse and fine spatial information.