Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers

• URL: http://arxiv.org/abs/2606.21289v1
• Date: Fri, 19 Jun 2026 10:13:18 GMT
• Title: Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers
• Abstract summary: Deep learning has been introduced into vibrational spectroscopy showing great potential.<n>To tackle this, we propose a task-enhanced augmentation network (TeaNet)<n>Results on both synthetic and real-world datasets verified the superiority of the proposed method.
• Score: 21.143320422817386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nondestructive detection methods, based on vibrational spectroscopy, are vitally important in a wide range of applications including industrial chemistry, pharmacy and national defense. Recently, deep learning has been introduced into vibrational spectroscopy showing great potential. Different from images, text, etc. that offer large labeled data sets, vibrational spectroscopic data is very limited, which requires novel concepts beyond transfer and meta learning. To tackle this, we propose a task-enhanced augmentation network (TeaNet). The key component of TeaNet is a reconstruction module that inputs randomly masked spectra and outputs reconstructed samples that are similar to the original ones, but include additional variations learned from the domain. These augmented samples are used to train the classification model. The reconstruction and prediction parts are trained simultaneously, end-to-end with back-propagation. Results on both synthetic and real-world datasets verified the superiority of the proposed method. In the most difficult synthetic scenarios TeaNet outperformed CNN by 17%. We visualized and analysed the neuron responses of TeaNet and CNN, and found that TeaNet's ability to identify discriminant wavenumbers was excellent compared to CNN. Our approach is general and can be easily adapted to other domains, offering a solution to more accurate and interpretable few-shot learning.

Related papers

• Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence [1.7961156132737741]
  Transfer learning (TL) is a powerful tool for enhancing the performance of neural networks (NNs) in applications such as weather and climate prediction and turbulence modeling.<n>In this study, we employ a 9-layer convolutional NN to predict the subgrid forcing in a two-layer ocean quasi-geostrophic system.<n>By re-training only one layer with data from the target system, this underestimation is corrected, enabling the NN to produce predictions that match the target spectra.
  arXiv  Detail & Related papers  (2025-04-21T23:42:19Z)
• Improving Network Interpretability via Explanation Consistency Evaluation [56.14036428778861]
  We propose a framework that acquires more explainable activation heatmaps and simultaneously increase the model performance. Specifically, our framework introduces a new metric, i.e., explanation consistency, to reweight the training samples adaptively in model learning. Our framework then promotes the model learning by paying closer attention to those training samples with a high difference in explanations.
  arXiv  Detail & Related papers  (2024-08-08T17:20:08Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• Femtosecond pulse parameter estimation from photoelectron momenta using machine learning [0.0]
  convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we test CNNs on strong-field ionization photoelectron spectra, training on theoretical data sets to invert' experimental data.
  arXiv  Detail & Related papers  (2023-03-24T11:58:06Z)
• SAR Despeckling Using Overcomplete Convolutional Networks [53.99620005035804]
  despeckling is an important problem in remote sensing as speckle degrades SAR images. Recent studies show that convolutional neural networks(CNNs) outperform classical despeckling methods. This study employs an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images.
  arXiv  Detail & Related papers  (2022-05-31T15:55:37Z)
• SiPRNet: End-to-End Learning for Single-Shot Phase Retrieval [8.820823270160695]
  convolutional neural networks (CNN) have played important roles in various image reconstruction tasks. In this paper, we design a novel CNN structure, named SiPRNet, to recover a signal from a single Fourier intensity measurement. The proposed approach consistently outperforms other CNN-based and traditional optimization-based methods in single-shot maskless phase retrieval.
  arXiv  Detail & Related papers  (2022-05-23T16:24:52Z)
• Classification of diffraction patterns using a convolutional neural network in single particle imaging experiments performed at X-ray free-electron lasers [53.65540150901678]
  Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is particularly well suited to determine the 3D structure of particles in their native environment. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. We propose to formulate this task as an image classification problem and solve it using convolutional neural network (CNN) architectures.
  arXiv  Detail & Related papers  (2021-12-16T17:03:14Z)
• NeRF in detail: Learning to sample for view synthesis [104.75126790300735]
  Neural radiance fields (NeRF) methods have demonstrated impressive novel view synthesis. In this work we address a clear limitation of the vanilla coarse-to-fine approach -- that it is based on a performance and not trained end-to-end for the task at hand. We introduce a differentiable module that learns to propose samples and their importance for the fine network, and consider and compare multiple alternatives for its neural architecture.
  arXiv  Detail & Related papers  (2021-06-09T17:59:10Z)
• Spectral Analysis Network for Deep Representation Learning and Image Clustering [53.415803942270685]
  This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis. It can identify the local similarities among images in patch level and thus more robust against occlusion. It can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples.
  arXiv  Detail & Related papers  (2020-09-11T05:07:15Z)
• Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms [8.747840760772268]
  We show the impact of data augmentation on the binary classification task of surgical mask detection in samples of human voice. Results show that most of the baselines given by ComParE are outperformed.
  arXiv  Detail & Related papers  (2020-08-11T09:02:47Z)
• Improving Deep Hyperspectral Image Classification Performance with Spectral Unmixing [3.84448093764973]
  We propose an abundance-based multi-HSI classification method. We convert every HSI from the spectral domain to the abundance domain by a dataset-specific autoencoder. Secondly, the abundance representations from multiple HSIs are collected to form an enlarged dataset.
  arXiv  Detail & Related papers  (2020-04-01T17:14:05Z)

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.