OvA-INN: Continual Learning with Invertible Neural Networks

TL;DR

OvA-INN introduces a novel continual learning approach using invertible neural networks for class-by-class training without data storage, outperforming existing methods on MNIST and CIFAR-100 datasets.

Contribution

The paper presents OvA-INN, a new method that trains class-specific invertible neural networks for continual learning without data replay or task labels.

Findings

Achieves 72% accuracy on CIFAR-100 after learning classes sequentially.

Outperforms state-of-the-art methods on MNIST and CIFAR-100 datasets.

Effectively leverages pretrained feature extractors with invertible 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 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.