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Zero-shot learning (ZSL) facilitates the transfer of knowledge from seen to unseen categories through high-dimensional vectors that capture both known and unknown class names. However it encounters challenges with domain shift arising from a lack of sufficient labeled data. Although transductive zero-shot learning (TZSL) addresses this bias by including samples from unseen classes, it still faces obstacles in enhancing TZSL performance. In this study, We introduce the Structure Alignment Variational Autoencoder Generative Adversarial Network (SA-VAEGAN), a novel approach that enhances the alignment between visual and auxiliary spaces. We delved into the underlying causes of domain shift and introduced a structural alignment (SA) strategy to tackle these challenges. The SA model thoroughly accounts for both inter-class and intra-class dynamics, designed to leverage the model's comprehension of high-level semantic relations to disambiguate confusion among similar classes and mitigate intra-class confusion by penalizing atypical visual samples within classes. Assessed across four benchmark datasets, SA-VAEGAN has established a new performance standard, underscoring its efficiency in addressing the domain shift challenge within TZSL tasks, and achieving high accuracy. • We introduce the concept of root-oriented optimization models for the first time. • We deploy a generative model integrated with the structure alignment strategy. • The proposed model outperforms state-of-the-art methods on four benchmarks. [ABSTRACT FROM AUTHOR]

• Present new generalization risk bounds for multi-class domain adaptation. • Proposed a novel learning method based on the risk bounds. • Performed an empirical study on benchmarking data sets. • Considering class-conditional domain invariance delivers better performances. Neural network-based models have recently shown excellent performance in various kinds of tasks. However, a large amount of labeled data is required to train deep networks, and the cost of gathering labeled training data for every kind of domain is prohibitively expensive. Domain adaptation tries to solve this problem by transferring knowledge from labeled source domain data to unlabeled target domain data. Previous research tried to learn domain-invariant features of source and target domains to address this problem, and this approach has been used as a key concept in various methods. However, domain-invariant features do not mean that a classifier trained on source data can be directly applied to target data because it does not guarantee that data distribution of the same classes will be aligned across two domains. In this paper, we present novel generalization upper bounds for domain adaptation that motivates the need for class-conditional domain invariant learning. Based on this theoretical framework, we then propose a class-conditional domain invariant learning method that can learn a feature space in which features in the same class are expected to be mapped nearby. We empirically experimented that our model showed state-of-the-art performance on standard datasets and showed effectiveness by visualization of latent space. [ABSTRACT FROM AUTHOR]