论文标题
通过背景级正规化进行准确的开放式识别
Towards Accurate Open-Set Recognition via Background-Class Regularization
论文作者
论文摘要
在开放式识别（OSR）中，分类器应能够拒绝不知名的样本，同时保持高闭合定点的分类精度。为了有效解决OSR问题，先前的研究试图通过离线分析，例如基于距离的特征分析或复杂的网络体系结构来限制有限空间外部的潜在特征空间，并拒绝位于有限空间之外的数据。为了通过标准分类器体系结构中的简单推理过程（无脱机分析）进行OSR，我们使用基于距离的分类器代替常规的SoftMax分类器。之后，我们设计了一种背景级正规化策略，该策略在训练阶段使用背景级数据作为不知名级的替代策略。具体而言，我们制定了适合基于距离的分类器的新型正则化损失，该损失可为已知类别和迫使背景级样品远离有限空间的背景阶级样本提供足够大的较大的类潜在特征空间。通过我们的广泛实验，我们表明所提出的方法可提供强大的OSR结果，同时保持高闭合分类的精度。
In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. To effectively solve the OSR problem, previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses, e.g., distance-based feature analyses, or complicated network architectures. To conduct OSR via a simple inference process (without offline analyses) in standard classifier architectures, we use distance-based classifiers instead of conventional Softmax classifiers. Afterwards, we design a background-class regularization strategy, which uses background-class data as surrogates of unknown-class ones during training phase. Specifically, we formulate a novel regularization loss suitable for distance-based classifiers, which reserves sufficiently large class-wise latent feature spaces for known classes and forces background-class samples to be located far away from the limited spaces. Through our extensive experiments, we show that the proposed method provides robust OSR results, while maintaining high closed-set classification accuracy.
