Towards Neural Network Patching: Evaluating Engagement-Layers and Patch-Architectures

Understand

In this report we investigate fundamental requirements for the application of classifier patching on neural networks.

• Neural network patching is an approach for adapting neural network models to handle concept drift in nonstationary environments.
• Instead of creating or updating the existing network to accommodate concept drift, neural network patching leverages the inner layers of the network as well as its output to learn a patch that enhances the classification and corrects errors caused by the drift.
• It learns (i) a predictor that estimates whether the original network will misclassify an instance, and (ii) a patching network that fixes the misclassification.