Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

We study incremental learning for the classification task, a key component for life-long learning systems. For an incremental learning algorithm, the main challenges are to update the classifier whilst preserving previous knowledge. In addition to forgetting, a well-known issue while preserving knowledge, we observe that incremental learning algorithms also suffer from a crucial problem of intransigence, its inability to update knowledge. First, we introduce two metrics to quantify forgetting and intransigence that allow us to understand, analyse, and gain better insights into the behaviour of an incremental learning algorithm. Second, we present a generalization of EWC and Path Integral, with a theoretically grounded KL-divergence based perspective. We thoroughly analyse and compare the behaviour of different incremental learning algorithms on MNIST and CIFAR-100 datasets. We obtain superior results for our method in terms of accuracy, and provide better trade-off for forgetting and intransigence.