In the problem of generalized zero-shot learning, the datapoints from unknown
classes are not available during training. The main challenge for generalized
zero-shot learning is the unbalanced data distribution which makes it hard for
the classifier to distinguish if a given testing sample comes from a seen or
unseen class. However, using Generative Adversarial Network (GAN) to generate
auxiliary datapoints by the semantic embeddings of unseen classes alleviates
the above problem. Current approaches combine the auxiliary datapoints and
original training data to train the generalized zero-shot learning model and
obtain state-of-the-art results. Inspired by such models, we propose to feed
the generated data via a model selection mechanism. Specifically, we leverage
two sources of datapoints (observed and auxiliary) to train some classifier to
recognize which test datapoints come from seen and which from unseen classes.
This way, generalized zero-shot learning can be divided into two disjoint
classification tasks, thus reducing the negative influence of the unbalanced
data distribution. Our evaluations on four publicly available datasets for
generalized zero-shot learning show that our model obtains state-of-the-art
results.

通过使用生成对抗网络（GAN）生成未见类别的语义嵌入辅助数据点，结合传统训练数据，利用模型选择机制使得泛零样本学习可以分为两个不相交的分类任务，以减少不平衡的数据分布，从而获得最先进的结果。