What is Neural ODE and how does it relate to continuous-depth vision models?

Neural ODE models treat deep networks as continuous transformations parameterized by differential equations.

Neural Ordinary Differential Equations (Neural ODEs) replace discrete layers with a continuous transformation defined by a learnable function f(x, t). Instead of stacking layers, the model integrates this function over time using an ODE solver. In vision, this allows adaptive computation depth, memory efficiency, and smoother feature transformations. It reframes deep learning as a continuous dynamical system rather than a fixed-depth computation graph.