Minimal Multi-Layer Modifications of Deep Neural Networks

TL;DR

This paper introduces 3M-DNN, a novel tool for repairing deep neural networks by minimally modifying multiple layers simultaneously to correct errors, using a sequence of black-box verification calls, especially in safety-critical applications.

Contribution

The paper presents the first method capable of repairing multiple layers in a DNN simultaneously through a layered splitting approach and minimal weight modifications.

Findings

Effective correction of erroneous behaviors in DNNs.

Minimal weight modifications preserve network performance.

Promising results on extensive benchmarks.

Abstract

Deep neural networks (DNNs) have become increasingly popular in recent years. However, despite their many successes, DNNs may also err and produce incorrect and potentially fatal outputs in safety-critical settings, such as autonomous driving, medical diagnosis, and airborne collision avoidance systems. Much work has been put into detecting such erroneous behavior in DNNs, e.g., via testing or verification, but removing these errors after their detection has received lesser attention. We present here a new tool, called 3M-DNN, for repairing a given DNN, which is known to err on some set of inputs. The novel repair procedure implemented in 3M-DNN computes a modification to the network's weights that corrects its behavior, and attempts to minimize this change via a sequence of calls to a backend, black-box DNN verification engine. To the best of our knowledge, our method is the first one that allows repairing the network by simultaneously modifying multiple layers. This is achieved by splitting the network into sub-networks, and applying a single-layer repairing technique to each component. We evaluated 3M-DNN tool on an extensive set of benchmarks, obtaining promising results.