Debugging using Orthogonal Gradient Descent

• URL: http://arxiv.org/abs/2206.08489v1
• Date: Fri, 17 Jun 2022 00:03:54 GMT
• Title: Debugging using Orthogonal Gradient Descent
• Authors: Narsimha Chilkuri, Chris Eliasmith
• Abstract summary: 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 " neural networks similar to how we address bugs in our mathematical models and standard computer code?
• Score: 7.766921168069532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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