Zero-Shot Learning (ZSL) seeks to recognize a sample from either seen or unseen domain by projecting the image data and semantic labels into a joint embedding space. However, most existing methods directly adapt a well-trained projection from one domain to another, thereby ignoring the serious bias problem caused by domain differences. To address this issue, we propose a novel Domain-Specific Embedding Network (DSEN) that can apply specific projections to different domains for unbiased embedding, as well as several domain constraints. In contrast to previous methods, the DSEN decomposes the domain-shared projection function into one domain-invariant and two domain-specific sub-functions to explore the similarities and differences between two domains. To prevent the two specific projections from breaking the semantic relationship, a semantic reconstruction constraint is proposed by applying the same decoder function to them in a cycle consistency way. Furthermore, a domain division constraint is developed to directly penalize the margin between real and pseudo image features in respective seen and unseen domains, which can enlarge the inter-domain difference of visual features. Extensive experiments on four public benchmarks demonstrate the effectiveness of DSEN with an average of $9.2\%$ improvement in terms of harmonic mean. The code is available in \url{https://github.com/mboboGO/DSEN-for-GZSL}.

提出了一种基于领域特定嵌入网络（DSEN）的零样本学习方法，通过将图像数据和语义标签投影到联合嵌入空间中进行识别，该方法分解了领域共享投影函数并使用领域特定子函数探索领域的相似性和差异性，同时引入语义重构和领域划分约束以解决偏倚问题和保持语义关联。实验表明，DSEN在四个公共基准测试中具有显著的改进。

领域特定的嵌入网络用于零样本识别