Debugging using Orthogonal Gradient Descent

Updatable Machine Learning, ICML, 2022

Narsimha Chilkuri, Chris Eliasmith


In this report we consider the following problem: Given a trained model that is partially faulty, can we correct its behaviour without having to train the model from scratch? In other words, can we “debug" neural networks similar to how we address bugs in our mathematical models and standard computer code. We base our approach on the hypothesis that debugging can be treated as a two-task continual learning problem. In particular, we employ a modified version of a continual learning algorithm called Orthogonal Gradient Descent (OGD) to demonstrate, via two simple experiments on the MNIST dataset, that we can in-fact \textit unlearn the undesirable behaviour while retaining the general performance of the model, and we can additionally \textit relearn the appropriate behaviour, both without having to train the model from scratch.

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