Assuming unknown classes could be present during classification, the open set
recognition (OSR) task aims to classify an instance into a known class or
reject it as unknown. In this paper, we use a two-stage training strategy for
the OSR problems. In the first stage, we introduce a self-supervised feature
decoupling method that finds the content features of the input samples from the
known classes. Specifically, our feature decoupling approach learns a
representation that can be split into content features and transformation
features. In the second stage, we fine-tune the content features with the class
labels. The fine-tuned content features are then used for the OSR problems.
Moreover, we consider an unsupervised OSR scenario, where we cluster the
content features learned from the first stage. To measure representation
quality, we introduce intra-inter ratio (IIR). Our experimental results
indicate that our proposed self-supervised approach outperforms others in image
and malware OSR problems. Also, our analyses indicate that IIR is correlated
with OSR performance.

本文提出了一种自监督特征解耦方法，用于开放集识别任务，通过利用聚类算法来同步解决训练数据不足和未知类别问题。实验结果表明，自监督方法在图像识别和恶意软件检测的任务中能够优于其他方法，同时提出的内外比率指标与开放集识别的性能相关。