3 ms·
Dang! I started working mid-last year on practically exactly this as a side project (was going to call the paper "Towards the horse", if you get the reference :
by m_que 7y ago
Dang! I started working mid-last year on practically exactly this as a side project (was going to call the paper "Towards the horse", if you get the reference :) ). Congratulation for making it work, I am really psyched. Can you remember what triggered the motivation to work on this? Somehow I remember there being something on reaction-diffusion equations on HN around that time.
Had you tried to use a single Laplace operator instead of two Sobel filters? My approach is to model the reaction-diffusion as a sum of 1x1-convolution (reaction) and a depthwise 3x3 convolution with a fixed kernel multiplied by a learnable constant (diffusion). However, for this to work, a single seed pixel obviously will not work. Any thoughts?
- m_que 7y agoNow having scrolled down to the bottom of the papaer, I have seen a section on the genesis of the idea.
- eyvindn 7y agoDiffusion-reaction systems were an inspiration in general. Using a Laplace operator (or the discretised equivalent for our 2D grid) might have trouble learning to generate these patterns - the Laplacian wouldn’t always provide unique information as to where a cell might be based on its neighbours. It’s possible the network would learn to exploit the hidden channels to bypass this directional invariance. Starting from a single pixel in such a setting would indeed need some mechanism to break the symmetry (stochastic updates as used here, for instance).