How do CNN architectures scale to very deep networks without losing performance?

CNNs scale using residual connections, normalization layers, and careful initialization to prevent optimization issues.

Deep CNNs suffer from vanishing gradients and optimization instability. Modern architectures solve this using residual connections (ResNet), normalization techniques like batch norm, and improved weight initialization. These mechanisms ensure stable gradient flow and prevent degradation in deeper models.