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.

翻译：开放世界模式识别涉及对已知类别的模式进行分类，并拒绝模糊和新型（也称为分布外（OOD））输入。深度神经网络模型通常在封闭集分类中表现出色，但在拒绝OOD方面表现不佳。为解决这一问题，众多方法被设计用于执行开放集识别（OSR）或OOD拒绝/检测任务。以往方法多采用训练后得分变换或混合模型，以确保在分离已知类别的同时，对OOD输入赋予低得分。本文尝试构建一个统一的框架，以构建同时适用于分类与OOD拒绝的开放集分类器。我们将具有$ K $个已知类别的开放集识别问题建模为一个$(K+1)$类分类问题，且模型仅使用已知类别样本训练。通过将$ K $类问题分解为$ K $个一对多（OVA）二分类任务，并绑定部分参数，我们证明组合OVA分类器的得分可以给出$(K+1)$类的后验概率，从而在统一框架内实现分类与OOD拒绝。为保持OVA训练分类器的封闭集分类精度，我们提出了一种结合OVA损失与多分类交叉熵损失的混合训练策略。我们在最近提出的卷积原型网络上实现了OVA框架与混合训练策略。在流行的OSR和OOD检测数据集上的实验表明，所提框架仅使用单个多分类器，即可在封闭集分类、OOD检测和误分类检测方面取得竞争性性能。