While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training. To successfully learn in a label-agnostic incremental setting, a model must distinguish between learned and novel classes to properly include samples for training. We introduce a novelty detection method that leverages network confusion caused by training incoming data as a new class. We found that incorporating a class-imbalance during this detection method substantially enhances performance. The effectiveness of our approach is demonstrated across a set of image classification benchmarks: MNIST, SVHN, CIFAR-10, CIFAR-100, and CRIB.
@article{arxiv.2104.04450,
title = {Unsupervised Class-Incremental Learning Through Confusion},
author = {Shivam Khare and Kun Cao and James Rehg},
journal= {arXiv preprint arXiv:2104.04450},
year = {2021}
}