Classifying patterns of known classes and rejecting ambiguous and novel (also
called as out-of-distribution (OOD)) inputs are involved in open world pattern
recognition. Deep neural network models usually excel in closed-set
classification while performing poorly in rejecting OOD. To tackle this
problem, numerous methods have been designed to perform open set recognition
(OSR) or OOD rejection/detection tasks. Previous methods mostly take
post-training score transformation or hybrid models to ensure low scores on OOD
inputs while separating known classes. In this paper, we attempt to build a
unified framework for building open set classifiers for both classification and
OOD rejection. We formulate the open set recognition of $ K $-known-class as a
$ (K + 1) $-class classification problem with model trained on known-class
samples only. By decomposing the $ K $-class problem into $ K $ one-versus-all
(OVA) binary classification tasks and binding some parameters, we show that
combining the scores of OVA classifiers can give $ (K + 1) $-class posterior
probabilities, which enables classification and OOD rejection in a unified
framework. To maintain the closed-set classification accuracy of the OVA
trained classifier, we propose a hybrid training strategy combining OVA loss
and multi-class cross-entropy loss. We implement the OVA framework and hybrid
training strategy on the recently proposed convolutional prototype network.
Experiments on popular OSR and OOD detection datasets demonstrate that the
proposed framework, using a single multi-class classifier, yields competitive
performance in closed-set classification, OOD detection, and misclassification
detection.

通过使用统一框架和混合训练策略，本文在开放世界模式识别中建立了一个能够同时进行分类和拒绝未知输入的开放集分类器，实验证明该方法在闭集分类、未知输入检测和错误分类检测中表现竞争性。