  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.
