Title: Lipschitz-Bounded Equilibrium Networks. Abstract: This paper introduces new parameterizations of equilibrium neural networks, i.e.
networks defined by implicit equations. This model class includes standard multilayer
and residual networks as special cases. The new parameterization admits
a Lipschitz bound during training via unconstrained optimization, i.e. no projections
or barrier functions are required. Lipschitz bounds are a common proxy for
robustness and appear in many generalization bounds. Furthermore, compared to
previous works we show well-posedness (existence of solutions) under less restrictive
conditions on the network weights and more natural assumptions on the
activation functions: that they are monotone and slope restricted. These results
are proved by establishing novel connections with convex optimization, operator
splitting on non-Euclidean spaces, and contracting neural ODEs. In image classification
experiments we show that the Lipschitz bounds are very accurate and
improve robustness to adversarial attacks.