Unsupervised Class-Incremental Learning through Confusion
Authors: Shivam Khare, Kun Cao, James Matthew Rehg
Keywords: Incremental Learning, Unsupervised Learning, Continual Learning, Novelty Detection, Out-of-Distribution Detection
Abstract: 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 common image classification benchmarks: MNIST, SVHN, CIFAR-10, and CIFAR-100.