Title: OvA-INN: Continual Learning with Invertible Neural Networks. Abstract: 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 output the zero vector for its class. At test time, we can predict the class of a sample by identifying which network outputs the vector with the smallest norm. With this method, we show that we can take advantage of pretrained models by stacking an invertible network on top of a features 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 are reaching 72% accuracy on CIFAR-100 after training our model one class at a time.