论文标题
域细分和调整，用于广义零击学习
Domain segmentation and adjustment for generalized zero-shot learning
论文作者
论文摘要
在广义的零射门学习中，将看不见的数据与生成模型合成是解决可见类和看不见类之间训练数据不平衡的最流行方法。但是，这种方法要求在训练阶段可获得看不见的语义信息，并且训练生成模型并不是很微不足道的。鉴于这些模型的生成器只能通过可见的类培训，我们认为合成未见数据可能不是解决训练数据不平衡引起的域移位的理想方法。在本文中，我们建议在不同域中实现广泛的零射击识别。因此，看不见的（见）类可以避免看到（看不见的）类的影响。实际上，我们提出了一种阈值和概率分布联合方法，将测试实例分为可见的，看不见和不确定的域。此外，进一步调整了不确定的域以减轻域的转移。在五个基准数据集上进行的广泛实验表明，与基于生成模型相比，该提出的方法具有竞争性能。
In the generalized zero-shot learning, synthesizing unseen data with generative models has been the most popular method to address the imbalance of training data between seen and unseen classes. However, this method requires that the unseen semantic information is available during the training stage, and training generative models is not trivial. Given that the generator of these models can only be trained with seen classes, we argue that synthesizing unseen data may not be an ideal approach for addressing the domain shift caused by the imbalance of the training data. In this paper, we propose to realize the generalized zero-shot recognition in different domains. Thus, unseen (seen) classes can avoid the effect of the seen (unseen) classes. In practice, we propose a threshold and probabilistic distribution joint method to segment the testing instances into seen, unseen and uncertain domains. Moreover, the uncertain domain is further adjusted to alleviate the domain shift. Extensive experiments on five benchmark datasets show that the proposed method exhibits competitive performance compared with that based on generative models.
