Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Zero-shot incremental learning using spatial-frequency feature representations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月29日

查看全文
  • Jie Ren,
  • Yang Zhao,
  • Weichuan Zhang,
  • Changming Sun

摘要

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.