  Class incremental learning (CIL) aims to learn a model that can not only
incrementally accommodate new classes, but also maintain the learned knowledge
of old classes. Out-of-distribution (OOD) detection in CIL is to retain this
incremental learning ability, while being able to reject unknown samples that
are drawn from different distributions of the learned classes. This capability
is crucial to the safety of deploying CIL models in open worlds. However,
despite remarkable advancements in the respective CIL and OOD detection, there
lacks a systematic and large-scale benchmark to assess the capability of
advanced CIL models in detecting OOD samples. To fill this gap, in this study
we design a comprehensive empirical study to establish such a benchmark, named
$\textbf{OpenCIL}$. To this end, we propose two principled frameworks for
enabling four representative CIL models with 15 diverse OOD detection methods,
resulting in 60 baseline models for OOD detection in CIL. The empirical
evaluation is performed on two popular CIL datasets with six commonly-used OOD
datasets. One key observation we find through our comprehensive evaluation is
that the CIL models can be severely biased towards the OOD samples and newly
added classes when they are exposed to open environments. Motivated by this, we
further propose a new baseline for OOD detection in CIL, namely Bi-directional
Energy Regularization ($\textbf{BER}$), which is specially designed to mitigate
these two biases in different CIL models by having energy regularization on
both old and new classes. Its superior performance is justified in our
experiments. All codes and datasets are open-source at
https://github.com/mala-lab/OpenCIL.
