It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match. Although transductive ZSL (TZSL) attempts to improve this by allowing the use of unlabelled examples from the unseen classes, there is still a high level of distribution shift. We propose a novel TZSL model (named as Bi-VAEGAN), which largely improves the shift by a strengthened distribution alignment between the visual and auxiliary spaces. The key proposal of the model design includes (1) a bi-directional distribution alignment, (2) a simple but effective L_2-norm based feature normalization approach, and (3) a more sophisticated unseen class prior estimation approach. In benchmark evaluation using four datasets, Bi-VAEGAN achieves the new state of the arts under both the standard and generalized TZSL settings. Code could be found at https://github.com/Zhicaiwww/Bi-VAEGAN

翻译：众所周知， 零样本学习(ZSL)受到域偏移问题的严重影响，即用于未知类别的真实和学习数据分布不匹配。虽然跨领域零样本学习(TZSL)试图通过使用未知类别的无标签样本来改善这一点，但仍然存在较高的分布偏移水平。我们提出了一种新的TZSL模型（命名为Bi-VAEGAN），通过加强视觉和辅助空间之间的分布对齐来大大提高偏移问题。模型设计的关键提议包括(1)双向分布对齐，(2)一个简单但有效的L_2-范数特征归一化方法和(3)更复杂的未知类别先验估计方法。在基准测试中使用四个数据集，Bi-VAEGAN在标准和广义TZSL设置下均实现了新的最高精度。代码可在https://github.com/Zhicaiwww/Bi-VAEGAN中找到。