Minimal Multi-Layer Modifications of Deep Neural Networks

Understand

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.