Transformer and Graph Convolution-Based Unsupervised Detection of Machine Anomalous Sound Under Domain Shifts

Yan, Jingke; Cheng, Yao; Wang, Qin; Liu, Lei; Zhang, Weihua; Jin, Bo

doi:10.1109/tetci.2024.3377728

Cited by 23 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6 These methods are particularly suited for establishing mappings between HR and LR images, especially in handling the empirical distribution of complex images. In the context of SISR tasks, deep learning-based methods can be categorized into several types, primarily including generative adversarial networks (GANs), 7 variational autoencoders (VAEs), 8,9 and normalizing flows. 10 However, these existing deep learning approaches occasionally encounter challenges with modal collapse and demonstrate deficiencies in handling high-frequency details in images.…”

Section: Introductionmentioning

confidence: 99%

Improving image super-resolution with structured knowledge distillation-based multimodal denoising diffusion probabilistic model

Huang,

Yan,

Wang

et al. 2024

J. Electron. Imag.

Self Cite

View full text Add to dashboard Cite

In the realm of low-resolution (LR) to high-resolution (HR) image reconstruction, denoising diffusion probabilistic models (DDPMs) are recognized for their superior perceptual quality over other generative models, attributed to their adept handling of various degradation factors in LR images, such as noise and blur. However, DDPMs predominantly focus on a single modality in the super-resolution (SR) image reconstruction from LR images, thus overlooking the rich potential information in multimodal data. This lack of integration and comprehensive processing of multimodal data can impede the full utilization of the complementary characteristics of different data types, limiting their effectiveness across a broad range of applications. Moreover, DDPMs require thousands of evaluations to reconstruct high-quality SR images, which significantly impacts their efficiency. In response to these challenges, a novel multimodal DDPM based on structured knowledge distillation (MKDDPM) is introduced. This approach features a multimodal-based DDPM that effectively leverages sparse prior information from another modality, integrated into the MKDDPM network architecture to optimize the solution space and detail features of the reconstructed image. Furthermore, a structured knowledge distillation method is proposed, leveraging a well-trained DDPM and iteratively learning a new DDPM, with each iteration requiring only half the original sampling steps. This method significantly reduces the number of model sampling steps without compromising on sampling quality. Experimental results demonstrate that MKDDPM, even with a substantially reduced number of diffusion steps, still achieves superior performance, providing a novel solution for single-image SR tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving image super-resolution with structured knowledge distillation-based multimodal denoising diffusion probabilistic model

Huang,

Yan,

Wang

et al. 2024

J. Electron. Imag.

Self Cite

View full text Add to dashboard Cite

show abstract

Super-resolution reconstruction of single image for latent features

Wang,

Yan,

Cai

et al. 2024

Comp. Visual Media

View full text Add to dashboard Cite

Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks.

show abstract

Residual attention guided vision transformer with acoustic-vibration signal feature fusion for cross-domain fault diagnosis

Lian,

Wang,

et al. 2025

Advanced Engineering Informatics

View full text Add to dashboard Cite

Transformer and Graph Convolution-Based Unsupervised Detection of Machine Anomalous Sound Under Domain Shifts

Cited by 23 publications

References 28 publications

Improving image super-resolution with structured knowledge distillation-based multimodal denoising diffusion probabilistic model

Improving image super-resolution with structured knowledge distillation-based multimodal denoising diffusion probabilistic model

Super-resolution reconstruction of single image for latent features

Residual attention guided vision transformer with acoustic-vibration signal feature fusion for cross-domain fault diagnosis

Contact Info

Product

Resources

About