The primary assumption of conventional supervised learning or classification
is that the test samples are drawn from the same distribution as the training
samples, which is called closed set learning or classification. In many
practical scenarios, this is not the case because there are unknowns or unseen
class samples in the test data, which is called the open set scenario, and the
unknowns need to be detected. This problem is referred to as the open set
recognition problem and is important in safety-critical applications. We
propose to detect unknowns (or unseen class samples) through learning pairwise
similarities. The proposed method works in two steps. It first learns a closed
set classifier using the seen classes that have appeared in training and then
learns how to compare seen classes with pseudo-unseen (automatically generated
unseen class samples). The pseudo-unseen generation is carried out by
performing distribution shifting augmentations on the seen or training samples.
We call our method OPG (Open set recognition based on Pseudo unseen data
Generation). The experimental evaluation shows that the learned
similarity-based features can successfully distinguish seen from unseen in
benchmark datasets for open set recognition.

研究中提出了一种 Open set recognition based on Pseudo unseen data Generation (OPG) 方法，利用相似度学习，通过先学习一个 closed set classifier，再学习如何将已知类别分别与伪未知类别进行比较（通过数据分布转化生成的），从而检测未知类别（或未看到过的类别），实验结果表明，这种基于相似度特征的方法可成功区分开放性识别基准数据集中已知和未知的类别。