We will explain a mechanism for generating neural network glyphs, like the glyphs we use in human languages. Glyphs are purposeful marks, images with 2D structures used to communicate information. We will use neural networks to generate those structured images, by optimizing for robustness.
[Colab Notebook](https://colab.research.google.com/drive/1lJNVcM0w7LMYEBfz_Yx1C7svl0hVMIox?usp=sharing) | [Slides](https://docs.google.com/presentation/d/18mg9jTeMOB13ts61CRfcdsxTsdYiu7m4dYaNC2w36X0/edit?usp=sharing) | [Blog Post](https://ichko.github.io/emergent-structures-in-robust-message-passing) | [Github Repo](https://github.com/ichko/inverted-auto-encoder)
Emergent structures in noisy channel message-passing is a blog post I wrote explaining a few experiments I did with neural network image generation. The idea is that we want to generate images that are robust under some noise. So we generate images from random representations. We perturb them and we try to decode the initial representation. This leads to the generator learning to create images with 2D structures.
