  The exemplar-free class incremental learning requires classification models
to learn new class knowledge incrementally without retaining any old samples.
Recently, the framework based on parallel one-class classifiers (POC), which
trains a one-class classifier (OCC) independently for each category, has
attracted extensive attention, since it can naturally avoid catastrophic
forgetting. POC, however, suffers from weak discriminability and comparability
due to its independent training strategy for different OOCs. To meet this
challenge, we propose a new framework, named Discriminative and Comparable
One-class classifiers for Incremental Learning (DisCOIL). DisCOIL follows the
basic principle of POC, but it adopts variational auto-encoders (VAE) instead
of other well-established one-class classifiers (e.g. deep SVDD), because a
trained VAE can not only identify the probability of an input sample belonging
to a class but also generate pseudo samples of the class to assist in learning
new tasks. With this advantage, DisCOIL trains a new-class VAE in contrast with
the old-class VAEs, which forces the new-class VAE to reconstruct better for
new-class samples but worse for the old-class pseudo samples, thus enhancing
the comparability. Furthermore, DisCOIL introduces a hinge reconstruction loss
to ensure the discriminability. We evaluate our method extensively on MNIST,
CIFAR10, and Tiny-ImageNet. The experimental results show that DisCOIL achieves
state-of-the-art performance.
