Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning
In quantum machine field, detecting two-dimensional (2D) materials in Silicon chips is one of the most critical problems. Instance segmentation can be considered as a potential approach to solve this problem. However, similar to other deep learning methods, the instance segmentation requires a large scale training dataset and high quality annotation in order to achieve a considerable performance. In practice, preparing the training dataset is a challenge since annotators have to deal with a large image, e.g 2K resolution, and extremely dense objects in this problem. In this work, we present a novel method to tackle the problem of missing annotation in instance segmentation in 2D quantum material identification. We propose a new mechanism for automatically detecting false negative objects and an attention based loss strategy to reduce the negative impact of these objects contributing to the overall loss function. We experiment on the 2D material detection datasets, and the experiments show our method outperforms previous works.
Code (0)
등록된 구현이 없습니다.
Tasks
2kInstance SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification
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. Th…
Knowledge DistillationContinual LearningSA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction
Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for hig…
Graph AttentionProperty PredictionSymbolic RegressionQuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
Characterizing two-dimensional quantum materials from optical microscopy images is challenging due to the subtle layer-dependent contrast, limited labeled data, and significant variation across laboratories and imaging s…
A Self-Attention Ansatz for Ab-initio Quantum Chemistry
We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer), which can be used as an approximation (or Ansatz) for solving the many-electron Schr\"odinger equation, the f…
QSAN: A Near-term Achievable Quantum Self-Attention Network
Self-Attention Mechanism (SAM) is good at capturing the internal connections of features and greatly improves the performance of machine learning models, espeacially requiring efficient characterization and feature extra…
Binary Classificationimage-classificationImage ClassificationModel Optimization+2