CLIFF：用于二维材料识别中增量 Flake 特征的持续学习
CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification
AI总结：
CLIFF通过冻结预训练模型并动态学习新材料特征，实现二维材料识别中的持续学习，有效降低遗忘率并提升分类精度。
摘要：
识别量子flake对于可扩展的量子硬件至关重要；然而，由于不同材料之间显著的外观变化，从光学显微镜自动分类层仍然具有挑战性。本文提出了一种新的flake层分类持续学习框架（CLIFF）。据我们所知，这项工作是首次系统研究二维材料中的持续学习。所提出的框架通过冻结背骨和基础头部（在参考材料上训练）使模型能够区分材料及其物理和光学性质。对于每种新材料，它学习特定材料的提示、嵌入和delta头部。提示池和余弦相似度门调节特征并计算特定材料的修正。此外，还集成了记忆回放与知识蒸馏。CLIFF在显著低于朴素微调和基于提示的基线的遗忘率下实现了具有竞争力的准确性。
英文摘要：
Identifying quantum flakes is crucial for scalable quantum hardware; however, automated layer classification from optical microscopy remains challenging due to substantial appearance shifts across different materials. This paper proposes a new Continual-Learning Framework for Flake Layer Classification (CLIFF). To the best of our knowledge, this work represents the first systematic study of continual learning in two-dimensional (2D) materials. The proposed framework enables the model to distinguish materials and their physical and optical properties by freezing the backbone and base head, which are trained on a reference material. For each new material, it learns a material-specific prompt, embedding, and a delta head. A prompt pool and a cosine-similarity gate modulate features and compute material-specific corrections. Additionally, memory replay with knowledge distillation is incorporated. CLIFF achieves competitive accuracy with significantly lower forgetting than naive fine-tuning and a prompt-based baseline.
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。
