Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in
  Class-incremental Information Extraction
Minqian Liu Lifu Huang
Class-incremental learning (CIL) aims to develop a learning system that can
continually learn new classes from a data stream without forgetting previously
learned classes. When learning classes incrementally, the classifier must be
constantly updated to incorporate new classes, and the drift in decision
boundary may lead to severe forgetting. This fundamental challenge, however,
has not yet been studied extensively, especially in the setting where no
samples from old classes are stored for rehearsal. In this paper, we take a
closer look at how the drift in the classifier leads to forgetting, and
accordingly, design four simple yet (super-) effective solutions to alleviate
the classifier drift: an Individual Classifiers with Frozen Feature Extractor
(ICE) framework where we individually train a classifier for each learning
session, and its three variants ICE-PL, ICE-O, and ICE-PL&O which further take
the logits of previously learned classes from old sessions or a constant logit
of an Other class as a constraint to the learning of new classifiers. Extensive
experiments and analysis on 6 class-incremental information extraction tasks
demonstrate that our solutions, especially ICE-O, consistently show significant
improvement over the previous state-of-the-art approaches with up to 44.7%
absolute F-score gain, providing a strong baseline and insights for future
research on class-incremental learning.