  This paper investigates a new, practical, but challenging problem named
Non-exemplar Online Class-incremental continual Learning (NO-CL), which aims to
preserve the discernibility of base classes without buffering data examples and
efficiently learn novel classes continuously in a single-pass (i.e., online)
data stream. The challenges of this task are mainly two-fold: (1) Both base and
novel classes suffer from severe catastrophic forgetting as no previous samples
are available for replay. (2) As the online data can only be observed once,
there is no way to fully re-train the whole model, e.g., re-calibrate the
decision boundaries via prototype alignment or feature distillation. In this
paper, we propose a novel Dual-prototype Self-augment and Refinement method
(DSR) for NO-CL problem, which consists of two strategies: 1) Dual class
prototypes: vanilla and high-dimensional prototypes are exploited to utilize
the pre-trained information and obtain robust quasi-orthogonal representations
rather than example buffers for both privacy preservation and memory reduction.
2) Self-augment and refinement: Instead of updating the whole network, we
optimize high-dimensional prototypes alternatively with the extra projection
module based on self-augment vanilla prototypes, through a bi-level
optimization problem. Extensive experiments demonstrate the effectiveness and
superiority of the proposed DSR in NO-CL.
