Zero-shot incremental learning using spatial-frequency feature representations

Zero-shot incremental learning aims to enable a 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 the ability to capture significant information from each sample image domain. Therefore, this paper proposes a novel spatial-frequency feature representation network (SFFRNet) that contains a spatial feature extraction (SFE) module and a frequency feature extraction (FFE) module to improve the zero-shot translation for the class incremental learning algorithm. The proposed SFFRNet 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 CIFAR-100 datasets demonstrate that our proposed algorithm outperforms state-of-the-art incremental learning algorithms.