In this article, we present a conceptually simple but effective framework called knowledge distillation classifier generation network (KDCGN) for zero-shot learning (ZSL), where the learning agent requires recognizing unseen classes that have no visual data for training. Different from the existing generative approaches that synthesize visual features for unseen classifiers’ learning, the proposed framework directly generates classifiers for unseen classes conditioned on the corresponding class-level semantics. To ensure the generated classifiers to be discriminative to the visual features, we borrow the knowledge distillation idea to both supervise the classifier generation and distill the knowledge with, respectively, the visual classifiers and soft targets trained from a traditional classification network. Under this framework, we develop two, respectively, strategies, i.e., class augmentation and semantics guidance, to facilitate the supervision process from the perspectives of improving visual classifiers. Specifically, the class augmentation strategy incorporates some additional categories to train the visual classifiers, which regularizes the visual classifier weights to be compact, under supervision of which the generated classifiers will be more discriminative. The semantics-guidance strategy encodes the class semantics into the visual classifiers, which would facilitate the supervision process by minimizing the differences between the generated and the real-visual classifiers. To evaluate the effectiveness of the proposed framework, we have conducted extensive experiments on five datasets in image classification, i.e., AwA1, AwA2, CUB, FLO, and APY. Experimental results show that the proposed approach performs best in the traditional ZSL task and achieves a significant performance improvement on four out of the five datasets in the generalized ZSL task.

中文翻译：

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用于零样本学习的知识蒸馏分类器生成网络


在本文中，我们提出了一个概念上简单但有效的框架，称为知识蒸馏分类器生成网络（KDCGN），用于零样本学习（ZSL），其中学习代理需要识别没有视觉数据进行训练的看不见的类。与现有的为看不见的分类器学习合成视觉特征的生成方法不同，所提出的框架直接根据相应的类级语义为看不见的类生成分类器。为了确保生成的分类器能够区分视觉特征，我们借用知识蒸馏的思想来监督分类器的生成，并分别使用从传统分类网络训练的视觉分类器和软目标来蒸馏知识。在此框架下，我们分别开发了两种策略，即类增强和语义指导，从改进视觉分类器的角度促进监督过程。具体来说，类增强策略结合了一些额外的类别来训练视觉分类器，将视觉分类器权重规范化为紧凑，在其监督下生成的分类器将更具辨别力。语义指导策略将类语义编码到视觉分类器中，这将通过最小化生成的视觉分类器和真实视觉分类器之间的差异来促进监督过程。为了评估所提出框架的有效性，我们对图像分类中的五个数据集（即 AwA1、AwA2、CUB、FLO 和 APY）进行了广泛的实验。 实验结果表明，所提出的方法在传统 ZSL 任务中表现最佳，并且在广义 ZSL 任务中的五个数据集中的四个上实现了显着的性能改进。