## 1 Introduction  
The high-speed development of deep learning is dependent on a large amount of labeled data. However, in the real world, the collection of large-scale labeled samples is a very difficult problem, and some specific categories do not have a large number of labeled samples, such as species with endangered statuses, and sample information with respect to these species is extremely difficult to obtain. In addition, canonical deep learning models can only recognize categories that have already been observed during the training phase (seen classes) and cannot recognize classes that have not been seen by the model (unseen classes). It is a challenge to train the model by using only samples from the seen classes to recognize samples from the unseen classes. Humans can use previous experiences to quickly learn new concepts. For example, suppose a child has never seen a zebra (zebra is an unseen class to child), but he knows that a zebra is a horse-like animal with black and white stripes (horse is a seen class to child); then, when he sees a zebra for the first time, he usually recognizes it as a zebra [1]. This is the core idea of the zero-shot learning approach, which is to use common sense or prior knowledge for deductive reasoning. The zero-shot learning (ZSL) [2] method provides a good solution to the above challenges. The traditional ZSL approach is based on the assumption that the test set contains only unseen classes, which is very easy to violate in reality. Therefore, a generalized zero-shot learning (GZSL) [3] approach emerged that is broader and more challenging than ZSL; that is, the test set contains seen and unseen classes. There is a challenge in combining the knowledge learned by humans with the model's rules. [PERSON] et al. [4] proposed a new vision of knowledge in AI models based on a combination of rules, learning, and human knowledge. This study attempts to provide \"form\" to the origins and development of artificial intelligence and thoroughly considers the ability of neural networks to learn from so-called hooks. GZSL is a method that combines human knowledge with model inference. Recent GZSL classification methods [5; 6; 7; 8; 9; 10] are usually based on generative models, such as generative adversarial networks (GAN) [11] and variational autoencoder (VAE) [12], etc. The goal of the generative model approach is to optimize the difference between real and generated data. Based on the sliding mode control, [PERSON] et al. [13] proposed a new strategy for a self-adjusting boundary layer using a robust controller to prevent the chattering phenomenon; this method can effectively suppress the transmitted noise and maintain the consistency of the system. Some other works [14; 15] use GAN to generate unseen features and also design auxiliary modules, such as decoders, classifier, etc. The reconstruction of semantic embeddings is performed during training to establish cycle-consistent constraints, and this auxiliary module allows the a generative model to synthesize semantic-consistent features. However, these constraints are only used in the training phase and are discarded in the classification phase; they are rarely used in VAE-based methods. These modules can synthesize discriminative features in the feature synthesis phase and can reduce ambiguities between categories in the classification phase. SDGZSL [10] learns a VAE by instances of the seen classes and the corresponding semantic embedding and then synthesizes the corresponding instances for the unseen classes by the trained VAE and trains a disentanglement module together with the seen classes instances to separate semantic-unrelated and semantic-consistent features. However, the focus on semantic-consistent in this study is only in the training phase, and semantic consistency is neglected in the feature generation and classification phases, resulting in synthesized unseen classes instances that do not represent their semantics well and visual features and corresponding semantic embeddings that are not well aligned in the modalities.