LärmStudienlage & Dosis

Top-Studien zu Lärm

Sortiert nach einflussreichen Zitationen und Erscheinungsjahr.

1. 01

   Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images.

   Proceedings of machine learning research·2026615 einflussreiche Zitationen

   Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated data. Predominantly used random masking methods make SSL less effective for medical images…

2. 02

   Discriminative and noise-robust embedding for zero-shot learning

   Neural Networks·2026

   Zero-shot learning (ZSL) seeks to empower models with the ability to identify novel classes by transferring knowledge from previously encountered categories. Despite promising progress, most current ZSL methods rely heavily on pre-trained…

3. 03

   Ensemble machine learning enhanced Raman spectroscopy for high accuracy and noise robust classification of carbamate pesticides

   Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy·2026

   The global rise in pesticide contamination demands innovative detection technologies to overcome the limitations of conventional methods. In response to the challenges, this work introduces a machine learning enhanced Raman spectroscopy…

4. 04

   Improving human motion generation based on a head-mounted display and its controllers via noise-augmented motion data and the recurrent inference model

   Neural Networks·2026

   Previous full-body human motion generation methods based on a head-mounted display (HMD) and two hand-held controllers rely on the assumption that the connections between the devices and body parts are rigid. Such approaches are not robust…

5. 05

   A noise-resilient distributed recurrent neural network for multi-agent consensus control and acoustic source localization

   Neural Networks·2026

   In multi-agent systems (MASs), consensus control and acoustic source localization are fundamental yet noise-sensitive tasks. While recurrent neural networks (RNNs) have shown strong potential in these domains due to their dynamic modeling…

6. 06

   Freezing pre-trained parameters of encoders for denoisers: Expanding pixel involvement and filtering out high-frequency noise

   Neural Networks·2026

   Image denoising is a pivotal and intricate task within the field of deep learning and the challenge of generalization poses significant difficulties for many denoisers when faced with out-of-distribution (OOD) noise. Model generalization…

7. 07

   Signal-to-noise ratio improvement in postmortem MRI using deep learning reconstruction (SwiftMR)

   Forensic Science International·2026

   Postmortem imaging has become an essential modality for death investigation, with computed tomography (PMCT) being widely adopted as a rapid and objective screening tool. However, the application of postmortem magnetic resonance imaging…

8. 08

   A noise robust and distribution-adaptive framework for multivariate time series anomaly detection

   Neural Networks·2026

   Existing unsupervised anomaly detection methods for multivariate time series(MTS) have demonstrated advanced performance on numerous public datasets. However, these methods exhibit two critical limitations: (1) the assumption of completely…

9. 09

   Three unbiased anisotropic diffusion filtering models with optimized parameters for Rician noise removing in MR images

   Magnetic Resonance Imaging·2026

   Due to the inherent correlation between Rice's noise and the Magnetic Resonance (MR) signal that occurs during the MR images formation, denoising these images is an important but complex process, as it can prove insufficient and even…

10. 10

    Noise estimation and suppression in quantitative EMCD measurements

    Ultramicroscopy·2026

    Quantitative electron magnetic circular dichroism (EMCD) in transmission electron microscopy (TEM) enables the measurement of magnetic moments with elemental and atomic site sensitivity, but its practical application is fundamentally…