Homogeneous Linear Inequality Constraints for Neural Network Activations

Thomas Frerix, Matthias Nießner, Daniel Cremers

We propose a method to impose homogeneous linear inequality constraints of the form $Ax\le 0$ on neural network activations. The proposed method allows a data-driven training appr…