A Conceptual Framework For White Box Neural Networks

This paper introduces semantic features as a general conceptual framework for fully explainable neural network layers. A well-motivated proof of concept model for relevant subproblem of MNIST consists of 4 such layers with the total of 4.8K learnable parameters. The model is easily interpretable, achieves human-level adversarial test accuracy with no form of adversarial training, requires little hyperparameter tuning and can be quickly trained on a single CPU. The general nature of the technique bears promise for a paradigm shift towards radically democratised and truly generalizable white box neural networks. The code is available at https://github.com/314-Foundation/white-box-nn