CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911477428912128 |
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| author | Pandey, Sankalp Nguyen, Xuan Bac Borys, Nicholas Churchill, Hugh Luu, Khoa |
| author_facet | Pandey, Sankalp Nguyen, Xuan Bac Borys, Nicholas Churchill, Hugh Luu, Khoa |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_17261 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification Pandey, Sankalp Nguyen, Xuan Bac Borys, Nicholas Churchill, Hugh Luu, Khoa Computer Vision and Pattern Recognition Machine Learning 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. |
| title | CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.17261 |