Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples, and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). A major challenge in GZSL is to learn consistently for those two different domains. Here we describe a probabilistic approach that breaks the model into three modular components, and then combines them in a consistent way. Specifically, our model consists of three classifiers: A "gating" model that softly decides if a sample is from a "seen" class and two experts: a ZSL expert, and an expert model for seen classes. We address two main difficulties in this approach: How to provide an accurate estimate of the gating probability without any training samples for unseen classes; and how to use an expert predictions when it observes samples outside of its domain. The key insight in our approach is to pass information between the three models to improve each others accuracy, while keeping the modular structure. We test our approach, Domain-Aware GZSL (DAZL) on three standard GZSL benchmark datasets (AWA, CUB, SUN), and find that it largely outperforms state-of-the-art GZSL models. DAZL is also the first model that closes the gap and surpasses the performance of generative models for GZSL, even-though it is a light-weight model that is much easier to train and tune.

本文介绍了一种基于概率的方法，将分类器分为三个组件，其中包括一个门控模型、一个零样本学习专家和一个针对已知类别的专家模型，以实现广义零样本学习（GZSL）。我们的方法解决了几个主要问题，包括如何为未知类别提供精确的门控概率估计和如何在其领域之外的样本中使用专家预测。我们测试的结果表明，我们的方法COSMO在四个标准GZSL基准数据集上表现良好，并且通常优于现有GZSL模型。

适应性置信度平滑技术用于广义零样本学习