In this paper, we try to investigate the dimensionality reduction ability of auto-encoder, and see if it has some kind of good property that might accumulate when being stacked and thus contribute to the success of deep learning. Based on the above idea, this paper starts from auto-encoder ...
However, the utilization of Ichimoku-based analysis faces challenges due to the high dimensionality of the datasets. Dimension reduction (DR) is a crucial technique in various fields due to the increasing complexity and volume of data. In [8], the authors emphasized the need for non-linear DR...
“以图搜图”正式的名称应该叫“相似图像搜索引擎”,也称为“反向图片搜索引擎”。 最初的图像搜索引擎是基于文本关键字检索的,因而这种方法本质上还是属于基于文本搜索引擎。 1992年,T. Kato提出了基于内容的图像检索(Content-Based Image Retrieval,CBIR)的概念,它使用图像的颜色、形状等信息作为特征构建索引以实现图...
more precisely, to explore the latent space. We will use the latter to perform feature extraction and dimensionality reduction. The implementation will be conducted using the Keras Functional API in Tensorflow 2.
[1]:19 is an artificial neural network used for learning efficient codings.[2][3] The aim of an auto-encoder is to learn a compressed, distributed representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Autoencoder is based on the concept of ...
n The target model copies all model designs with their parameters from the source model except the output layer, and fine-tunes these parameters based on the target dataset. In contrast, the output layer of the target model needs to be trained from scratch. ...
DAGMM consists of a deep AE to reduce the dimensionality of the input sample, and a GMM that is fed with the low-dimensional data provided by the AE. Furthermore, [6] describes an approach based on AE and GMM in which the objective functions of the optimization problem are transformed ...
Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction
Autoencoder based data-driven modeling approach is proposed for nonlinear materials. • Autoencoders enable noise filtering and dimensionality reduction of material data. • Convexity-preserving interpolation is employed for enhanced stability in data search. • Improved generalization capability is demo...
In this paper, autoencoders based deep learning model is proposed for image denoising. The autoencoders learns noise from the training images and then try to eliminate the noise for novel image. The experimental outcomes prove that this proposed model for PSNR has achieved higher result compared...