基于知识蒸馏的高光谱图像增强分类研究：遗忘问题探讨
HSI Image Enhancement Classification Based on Knowledge Distillation: A Study on Forgetting
AI总结：
本文提出基于教师模型的知识保留方法，通过增量类别样本缓解旧类别遗忘问题，并引入基于掩码的部分类别知识蒸馏算法，提升分类准确率。
摘要：
在高光谱图像的增量分类任务中，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这一问题，但它们严重依赖于旧类别的样本。本文提出了一种基于教师模型的知识保留方法，通过利用增量类别样本来缓解旧类别样本的模型遗忘，而不依赖于旧类别样本。此外，本文引入了一种基于掩码的部分类别知识蒸馏算法。通过解耦知识蒸馏，该方法过滤掉可能误导学生模型的潜在误导信息，从而提高整体准确率。比较实验和消融实验展示了所提方法的鲁棒性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
