5 research outputs found
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
Analyzing the worst-case performance of deep neural networks against input
perturbations amounts to solving a large-scale non-convex optimization problem,
for which several past works have proposed convex relaxations as a promising
alternative. However, even for reasonably-sized neural networks, these
relaxations are not tractable, and so must be replaced by even weaker
relaxations in practice. In this work, we propose a novel operator splitting
method that can directly solve a convex relaxation of the problem to high
accuracy, by splitting it into smaller sub-problems that often have analytical
solutions. The method is modular and scales to problem instances that were
previously impossible to solve exactly due to their size. Furthermore, the
solver operations are amenable to fast parallelization with GPU acceleration.
We demonstrate our method in obtaining tighter bounds on the worst-case
performance of large convolutional networks in image classification and
reinforcement learning settings