Abstract
In incremental classification tasks for hyperspectral images, catastrophic forgetting is an unavoidable challenge. While memory recall methods can mitigate this issue, they heavily rely on samples from old categories. This paper proposes a teacher-based knowledge retention method for incremental image classification. It alleviates model forgetting of old category samples by utilizing incremental category samples, without depending on old category samples. Additionally, this paper introduces a mask-based partial category knowledge distillation algorithm. By decoupling knowledge distillation, this approach filters out potentially misleading information that could misguide the student model, thereby enhancing overall accuracy. Comparative and ablation experiments demonstrate the proposed method's robust performance.
Abstract (translated)
在高光谱图像增量分类任务中，灾难性遗忘是一个不可避免的挑战。尽管记忆召回方法可以缓解此问题，但它们严重依赖旧类别的样本。本文提出了一种基于教师的知识保留方法用于增量图像分类，该方法通过利用增量类别样本来减轻模型对旧类别样本的遗忘，且不依赖旧类别样本。此外，本文引入了一种基于掩码的局部类别知识蒸馏算法，通过解耦知识蒸馏，该方法能够过滤可能误导学生模型的潜在错误信息，从而提升整体准确率。对比实验与消融实验均验证了所提方法的稳健性能。
URL
https://arxiv.org/abs/2603.20292
