🤖 AI Summary
This work addresses catastrophic forgetting in incremental hyperspectral image classification by proposing a knowledge retention method that operates without storing samples from previously seen classes. The approach leverages a teacher model and employs a masking mechanism to perform partial-class knowledge distillation using only the new-class data available in each incremental phase. This design effectively decouples the distillation process and filters out misleading information, thereby preserving relevant knowledge from earlier tasks. Without relying on rehearsal or replay of old samples, the proposed method significantly enhances both classification accuracy and model robustness. Its effectiveness is consistently validated through comprehensive comparative and ablation experiments across multiple benchmarks.
📝 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.