  Unsupervised lifelong learning refers to the ability to learn over time while
memorizing previous patterns without supervision. Although great progress has
been made in this direction, existing work often assumes strong prior knowledge
about the incoming data (e.g., knowing the class boundaries), which can be
impossible to obtain in complex and unpredictable environments. In this paper,
motivated by real-world scenarios, we propose a more practical problem setting
called online self-supervised lifelong learning without prior knowledge. The
proposed setting is challenging due to the non-iid and single-pass data, the
absence of external supervision, and no prior knowledge. To address the
challenges, we propose Self-Supervised ContrAstive Lifelong LEarning without
Prior Knowledge (SCALE) which can extract and memorize representations on the
fly purely from the data continuum. SCALE is designed around three major
components: a pseudo-supervised contrastive loss, a self-supervised forgetting
loss, and an online memory update for uniform subset selection. All three
components are designed to work collaboratively to maximize learning
performance. We perform comprehensive experiments of SCALE under iid and four
non-iid data streams. The results show that SCALE outperforms the
state-of-the-art algorithm in all settings with improvements up to 3.83%, 2.77%
and 5.86% in terms of kNN accuracy on CIFAR-10, CIFAR-100, and TinyImageNet
datasets.
