  Online continual learning for image classification studies the problem of
learning to classify images from an online stream of data and tasks, where
tasks may include new classes (class incremental) or data nonstationarity
(domain incremental). One of the key challenges of continual learning is to
avoid catastrophic forgetting (CF), i.e., forgetting old tasks in the presence
of more recent tasks. Over the past few years, many methods and tricks have
been introduced to address this problem, but many have not been fairly and
systematically compared under a variety of realistic and practical settings. To
better understand the relative advantages of various approaches and the
settings where they work best, this survey aims to (1) compare state-of-the-art
methods such as MIR, iCARL, and GDumb and determine which works best at
different experimental settings; (2) determine if the best class incremental
methods are also competitive in domain incremental setting; (3) evaluate the
performance of 7 simple but effective trick such as "review" trick and nearest
class mean (NCM) classifier to assess their relative impact. Regarding (1), we
observe earlier proposed iCaRL remains competitive when the memory buffer is
small; GDumb outperforms many recently proposed methods in medium-size datasets
and MIR performs the best in larger-scale datasets. For (2), we note that GDumb
performs quite poorly while MIR -- already competitive for (1) -- is also
strongly competitive in this very different but important setting. Overall,
this allows us to conclude that MIR is overall a strong and versatile method
across a wide variety of settings. For (3), we find that all 7 tricks are
beneficial, and when augmented with the "review" trick and NCM classifier, MIR
produces performance levels that bring online continual learning much closer to
its ultimate goal of matching offline training.
