Spectrum-BERT: Pre-training of Deep Bidirectional Transformers for
Spectral Classification of Chinese Liquors
- URL: http://arxiv.org/abs/2210.12440v1
- Date: Sat, 22 Oct 2022 13:11:25 GMT
- Title: Spectrum-BERT: Pre-training of Deep Bidirectional Transformers for
Spectral Classification of Chinese Liquors
- Authors: Yansong Wang, Yundong Sun, Yansheng Fu, Dongjie Zhu, Zhaoshuo Tian
- Abstract summary: We propose a pre-training method of deep bidirectional transformers for spectral classification of Chinese liquors, abbreviated as Spectrum-BERT.
We elaborately design two pre-training tasks, Next Curve Prediction (NCP) and Masked Curve Model (MCM), so that the model can effectively utilize unlabeled samples.
In the comparative experiments, the proposed Spectrum-BERT significantly outperforms the baselines in multiple metrics.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spectral detection technology, as a non-invasive method for rapid detection
of substances, combined with deep learning algorithms, has been widely used in
food detection. However, in real scenarios, acquiring and labeling spectral
data is an extremely labor-intensive task, which makes it impossible to provide
enough high-quality data for training efficient supervised deep learning
models. To better leverage limited samples, we apply pre-training & fine-tuning
paradigm to the field of spectral detection for the first time and propose a
pre-training method of deep bidirectional transformers for spectral
classification of Chinese liquors, abbreviated as Spectrum-BERT. Specifically,
first, to retain the model's sensitivity to the characteristic peak position
and local information of the spectral curve, we innovatively partition the
curve into multiple blocks and obtain the embeddings of different blocks, as
the feature input for the next calculation. Second, in the pre-training stage,
we elaborately design two pre-training tasks, Next Curve Prediction (NCP) and
Masked Curve Model (MCM), so that the model can effectively utilize unlabeled
samples to capture the potential knowledge of spectral data, breaking the
restrictions of the insufficient labeled samples, and improving the
applicability and performance of the model in practical scenarios. Finally, we
conduct a large number of experiments on the real liquor spectral dataset. In
the comparative experiments, the proposed Spectrum-BERT significantly
outperforms the baselines in multiple metrics and this advantage is more
significant on the imbalanced dataset. Moreover, in the parameter sensitivity
experiment, we also analyze the model performance under different parameter
settings, to provide a reference for subsequent research.
Related papers
- A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification [0.7864304771129751]
In this paper, we investigate the performance of a novel robust formulation for Support Vector Machine (SVM) in classifying COVID-19 samples obtained from Raman spectroscopy.
We derive robust counterpart models of deterministic formulations using bounded-by-norm uncertainty sets around each observation.
The effectiveness of our approach is validated on real-world COVID-19 datasets provided by Italian hospitals.
arXiv Detail & Related papers (2025-01-29T14:02:45Z) - DispFormer: Pretrained Transformer for Flexible Dispersion Curve Inversion from Global Synthesis to Regional Applications [59.488352977043974]
This study proposes DispFormer, a transformer-based neural network for inverting the $v_s$ profile from Rayleigh-wave phase and group dispersion curves.
Results indicate that zero-shot DispFormer, even without any labeled data, produces inversion profiles that match well with the ground truth.
arXiv Detail & Related papers (2025-01-08T09:08:24Z) - Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification [3.446873355279676]
classification of hyperspectral images (HSI) is a challenging task due to the high spectral dimensionality and limited labeled data.
We propose a novel multi-stage active transfer learning (ATL) framework that integrates a Spatial-Spectral Transformer (SST) with an active learning process for efficient HSI classification.
Experiments on benchmark HSI datasets demonstrate that the SST-ATL framework significantly outperforms existing CNN and SST-based methods.
arXiv Detail & Related papers (2024-11-27T07:53:39Z) - Point-Calibrated Spectral Neural Operators [54.13671100638092]
We introduce Point-Calibrated Spectral Transform, which learns operator mappings by approximating functions with the point-level adaptive spectral basis.
Point-Calibrated Spectral Neural Operators learn operator mappings by approximating functions with the point-level adaptive spectral basis.
arXiv Detail & Related papers (2024-10-15T08:19:39Z) - Downstream-Pretext Domain Knowledge Traceback for Active Learning [138.02530777915362]
We propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance.
DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator.
Experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods.
arXiv Detail & Related papers (2024-07-20T01:34:13Z) - DiffSpectralNet : Unveiling the Potential of Diffusion Models for
Hyperspectral Image Classification [6.521187080027966]
We propose a new network called DiffSpectralNet, which combines diffusion and transformer techniques.
First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features.
The diffusion method is capable of extracting diverse and meaningful spectral-spatial features, leading to improvement in HSI classification.
arXiv Detail & Related papers (2023-10-29T15:26:37Z) - Hodge-Aware Contrastive Learning [101.56637264703058]
Simplicial complexes prove effective in modeling data with multiway dependencies.
We develop a contrastive self-supervised learning approach for processing simplicial data.
arXiv Detail & Related papers (2023-09-14T00:40:07Z) - Machine learning enabled experimental design and parameter estimation
for ultrafast spin dynamics [54.172707311728885]
We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED)
Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED.
Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
arXiv Detail & Related papers (2023-06-03T06:19:20Z) - Exploring Supervised Machine Learning for Multi-Phase Identification and
Quantification from Powder X-Ray Diffraction Spectra [1.0660480034605242]
Powder X-ray diffraction analysis is a critical component of materials characterization methodologies.
Deep learning has become a prime focus for predicting crystallographic parameters and features from X-ray spectra.
Here, we are interested in conventional supervised learning algorithms in lieu of deep learning for multi-label crystalline phase identification.
arXiv Detail & Related papers (2022-11-16T00:36:13Z) - Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via
Simulation-based Synthetic Data Augmentation and Multitask Learning [4.633997895806144]
We consider quantitative analyses of spectral data using laser-induced breakdown spectroscopy.
We address the small size of training data available, and the validation of the predictions during inference on unknown data.
arXiv Detail & Related papers (2022-10-07T18:00:09Z) - Deep Semi-supervised Knowledge Distillation for Overlapping Cervical
Cell Instance Segmentation [54.49894381464853]
We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation.
We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining.
Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
arXiv Detail & Related papers (2020-07-21T13:27:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.