  Online Continual learning is a challenging learning scenario where the model
must learn from a non-stationary stream of data where each sample is seen only
once. The main challenge is to incrementally learn while avoiding catastrophic
forgetting, namely the problem of forgetting previously acquired knowledge
while learning from new data. A popular solution in these scenario is to use a
small memory to retain old data and rehearse them over time. Unfortunately, due
to the limited memory size, the quality of the memory will deteriorate over
time. In this paper we propose OLCGM, a novel replay-based continual learning
strategy that uses knowledge condensation techniques to continuously compress
the memory and achieve a better use of its limited size. The sample
condensation step compresses old samples, instead of removing them like other
replay strategies. As a result, the experiments show that, whenever the memory
budget is limited compared to the complexity of the data, OLCGM improves the
final accuracy compared to state-of-the-art replay strategies.
