Generalized zero-shot learning(GZSL) aims to classify samples from seen and unseen labels, assuming unseen labels are not accessible during training. Recent advancements in GZSL have been expedited by incorporating contrastive-learning-based (instance-based) embedding in generative networks and leveraging the semantic relationship between data points. However, existing embedding architectures suffer from two limitations: (1) limited discriminability of synthetic features' embedding without considering fine-grained cluster structures; (2) inflexible optimization due to restricted scaling mechanisms on existing contrastive embedding networks, leading to overlapped representations in the embedding space. To enhance the quality of representations in the embedding space, as mentioned in (1), we propose a margin-based prototypical contrastive learning embedding network that reaps the benefits of prototype-data (cluster quality enhancement) and implicit data-data (fine-grained representations) interaction while providing substantial cluster supervision to the embedding network and the generator. To tackle (2), we propose an instance adaptive contrastive loss that leads to generalized representations for unseen labels with increased inter-class margin. Through comprehensive experimental evaluation, we show that our method can outperform the current state-of-the-art on three benchmark datasets. Our approach also consistently achieves the best unseen performance in the GZSL setting.

翻译：广义零样本学习旨在对已知和未知标签的样本进行分类，其假设未知标签在训练期间不可访问。通过将基于对比学习（基于实例的）嵌入引入生成网络并利用数据点之间的语义关系，近期广义零样本学习的研究取得了进展。然而，现有嵌入架构面临两个局限性：（1）合成特征嵌入的区分性有限，未考虑细粒度聚类结构；（2）现有对比嵌入网络中的缩放机制受限导致优化缺乏灵活性，进而引发嵌入空间中的表征重叠。为解决（1）中提及的嵌入空间表征质量问题，我们提出了一种基于边界的原型对比学习嵌入网络，该网络能兼顾原型-数据（聚类质量增强）与隐式数据-数据（细粒度表征）交互的优势，同时为嵌入网络和生成器提供充分的聚类监督。针对（2），我们提出一种实例自适应对比损失函数，通过增大类间边界为未知标签生成泛化表征。通过全面的实验评估，我们证明该方法在三个基准数据集上均能超越当前最优性能。在广义零样本学习设置下，我们的方法始终取得最佳未知类别识别效果。