  In the field of Continual Learning, the objective is to learn several tasks
one after the other without access to the data from previous tasks. Several
solutions have been proposed to tackle this problem but they usually assume
that the user knows which of the tasks to perform at test time on a particular
sample, or rely on small samples from previous data and most of them suffer of
a substantial drop in accuracy when updated with batches of only one class at a
time. In this article, we propose a new method, OvA-INN, which is able to learn
one class at a time and without storing any of the previous data. To achieve
this, for each class, we train a specific Invertible Neural Network to extract
the relevant features to compute the likelihood on this class. At test time, we
can predict the class of a sample by identifying the network which predicted
the highest likelihood. With this method, we show that we can take advantage of
pretrained models by stacking an Invertible Network on top of a feature
extractor. This way, we are able to outperform state-of-the-art approaches that
rely on features learning for the Continual Learning of MNIST and CIFAR-100
datasets. In our experiments, we reach 72% accuracy on CIFAR-100 after training
our model one class at a time.
