  We have noticed that Marek et al. (2021) try to re-implement our paper Zheng
et al. (2020a) in their work "OodGAN: Generative Adversarial Network for
Out-of-Domain Data Generation". Our paper proposes a model to generate pseudo
OOD samples that are akin to IN-Domain (IND) input utterances. These pseudo OOD
samples can be used to improve the OOD detection performance by optimizing an
entropy regularization term when building the IND classifier. Marek et al.
(2021) report a large gap between their re-implemented results and ours on the
CLINC150 dataset (Larson et al., 2019). This paper discusses some key
observations that may have led to such a large gap. Most of these observations
originate from our experiments because Marek et al. (2021) have not released
their codes1. One of the most important observations is that stronger IND
classifiers usually exhibit a more robust ability to detect OOD samples. We
hope these observations help other researchers, including Marek et al. (2021),
to develop better OOD detectors in their applications.
