Zero-shot incremental learning aims to enable the model to generalize to new classes without forgetting previously learned classes. However, the semantic gap between old and new sample classes can lead to catastrophic forgetting. Additionally, existing algorithms lack capturing significant information from each sample image domain, impairing models' classification performance. Therefore, this paper proposes a novel Spatial-Frequency Domain Network (SFDNet) which contains a Spatial-Frequency Feature Extraction (SFFE) module and Attention Feature Alignment (AFA) module to improve the Zero-Shot Translation for Class Incremental algorithm. Firstly, SFFE module is designed which contains a dual attention mechanism for obtaining salient spatial-frequency feature information. Secondly, a novel feature fusion module is conducted for obtaining fused spatial-frequency domain features. Thirdly, the Nearest Class Mean classifier is utilized to select the most suitable category. Finally, iteration between tasks is performed using the Zero-Shot Translation model. The proposed SFDNet has the ability to effectively extract spatial-frequency feature representation from input images, improve the accuracy of image classification, and fundamentally alleviate catastrophic forgetting. Extensive experiments on the CUB 200-2011 and CIFAR100 datasets demonstrate that our proposed algorithm outperforms state-of-the-art incremental learning algorithms.

翻译：零样本增量学习旨在使模型在保留先前学习类别知识的同时，泛化至新类别。然而，新旧样本类别间的语义鸿沟可能导致灾难性遗忘。此外，现有算法缺乏从各样本图像域中捕获关键信息的能力，削弱了模型的分类性能。为此，本文提出一种新型空频域网络（SFDNet），该网络包含空频特征提取（SFFE）模块与注意力特征对齐（AFA）模块，用于改进类增量零样本翻译算法。首先，设计含双重注意力机制的SFFE模块，以获取显著的空频特征信息；其次，构建新型特征融合模块，用于生成融合空频域特征；再次，采用最近类均值分类器选择最适配类别；最后，通过零样本翻译模型执行任务间迭代。所提出的SFDNet能够有效提取输入图像的空频特征表示，提升图像分类精度，并从根本上缓解灾难性遗忘问题。在CUB 200-2011与CIFAR100数据集上的广泛实验表明，本文算法性能优于当前最先进的增量学习算法。