[2603.20292] HSI Image Enhancement Classification Based on Knowledge Distillation: A Study on Forgetting

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.20292 (cs) [Submitted on 19 Mar 2026] Title:HSI Image Enhancement Classification Based on Knowledge Distillation: A Study on Forgetting Authors:Songfeng Zhu View a PDF of the paper titled HSI Image Enhancement Classification Based on Knowledge Distillation: A Study on Forgetting, by Songfeng Zhu View PDF 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. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.20292 [cs.CV]   (or arXiv:2603.20292v1 [cs.CV] for this version)   https://doi.org/10.48550/arXiv.2603.20292 Focus to learn more arXiv-issue...