CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification
This addresses the challenge of appearance shifts in quantum flake identification for scalable quantum hardware, representing an incremental improvement in domain-specific continual learning.
The paper tackles the problem of automated layer classification for 2D materials from optical microscopy by proposing CLIFF, a continual learning framework that achieves competitive accuracy with significantly lower forgetting compared to baseline methods.
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. In this paper, we propose a new Continual-Learning Framework for Flake Layer Classification (CLIFF). To our knowledge, this is the first systematic study of continual learning in the domain of two-dimensional (2D) materials. Our method enables the model to differentiate between materials and their physical and optical properties by freezing a backbone and base head 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, we incorporate memory replay with knowledge distillation. CLIFF achieves competitive accuracy with significantly lower forgetting than naive fine-tuning and a prompt-based baseline.