论文标题
使用正交梯度下降调试
Debugging using Orthogonal Gradient Descent
论文作者
论文摘要
在本报告中，我们考虑以下问题：给定一个训练有素的模型，我们可以纠正其行为而无需从头开始训练模型吗？ 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事实上\ textit {unrearn}在保留模型的一般性能的同时，不希望的行为，我们还可以\ textit {recrearnn}适当的行为，而无需从头开始训练模型。
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.
