Autoencoder-based Representation Learning and Its Application in Intelligent Fault Diagnosis: A ReviewZheng Yang aBinbin Xu bWei Luo aFei Chen b
3.3.2.4 Deep auto-encoder (DAE) and its application in the SCM The DAE is a special DNN without class labels in which the input vectors and output vectors have equal dimensionality (Deng and Yu 2014). The initial goal of the auto-encoder is learning and representation (encoding) of the ...
Automotive Innovation (2023) 6:89–115 https://doi.org/10.1007/s42154-022-00205-0 Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems Caizhi Zhang1 · Weifeng Huang1 · Tong Niu1 · Zhitao Liu2 · Guofa Li1 · Dongpu...
It is therefore important to briefly present the basics of the autoencoder and its denoising version, before describing the deep learning architecture of Stacked (Denoising) Autoencoders. 2.3.1. Autoencoders An autoencoder is trained to encode the input x into a representation r(x) in a way...
[Nucleic Acids Research] Multiple sequence alignment-based RNA language model and its application to structural inference.[Paper],[Code] [Nature Methods] scGPT: toward building a foundation model for single-cell multi-omics using generative AI.[Paper],[Code] ...
We describe current shortcomings, enhancements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep residual networks, recurrent neural networks, reinforcement learning, variational autoencoders, and others. 展开 ...
MRI modality can provide complementary information due to its dependence on variable acquisition parameters, such as T1-weighted (T1), contrast-enhanced T1-weighted (T1c), T2-weighted (T2) and Fluid attenuation inversion recovery (Flair) images. T2 and Flair are suitable to detect the tumor ...
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection;Kasra Babaei, ZhiYuan Chen, Tomas Maul; This paper proposes an autoencoder (AE) that is used for improving the performance of once-class classifiers for the purpose of detecting anomalies. Traditional one-class classifiers (OCCs) ...
The breakthrough brought by generative adversarial networks (GANs) in computer vision (CV) applications has gained a lot of attention in different fields d
In the field of surface defect detection of industrial products, due to high accuracy and good adaptability, the supervised method is the most mainstream method in the current deep learning method, and its application scope is becoming wider and wider. However, the disadvantages of this kind of ...