The bimodal neural network model includes a spectrogram variational autoencoder and a text variational autoencoder. Output from the spectrogram variational autoencoder is used to influence output from text variational autoencoder.
一种常见的方法是合并这些重复的点,这样可以减少输入点云中的点数。 受G-PCC的启发,这篇文章将点云进行下采样后输再输入编码器。输入点 ,被最远点采样(FPS)得到子集 ,其中 是距离已采样点集 最远的点。与随机采样相比,FPS采样得到的点集的密度更均匀,更能保持原始对象的形状特征。 Encoder and Decoder 编码器...
One-class classification refers to approaches of learning using data from a single class only. In this paper, we propose a deep learning one-class classification method suitable for multimodal data, which relies on two convolutional autoencoders jointly trained to reconstruct the positive input data...
In this paper an approach based on a sparse autoencoder to assess the level of authentication achievable in a real indoor changing environment has been proposed. A real dataset was created by leveraging a Vectorial Signal Transceiver (VST), playing the role of the receiver, and two Universal ...
This paper proposes a deep learning-based scheme, named the CAEBN-HC, to address this issue. The proposed CAEBN-HC is designed based on the one-dimensional convolutional neural networks (1D-CNN) autoencoder and uses advanced training techniques, particularly the batch normalization (BN) and ...
We propose a novel filter for sparse big data, called an integrated autoencoder (IAE), which utilises auxiliary information to mitigate data sparsity. The proposed model achieves an appropriate balance between prediction accuracy, convergence speed, and complexity. We implement experiments on a GPS ...
Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike MAEs used in the image domain, where the pretext task is to restore features at the masked pixels,...
Benkert, M., Heroth, M., Herrler, R.et al.Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives.Auton. Intell. Syst.4, 8 (2024). https://doi.org/10.1007/s43684-024-00065-x ...
End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the ...
SDNE used a semi-supervised autoencoder for embedding the adjacency matrix, whose sparsity may generate more cost in the learning process. In order to solve these problems, we propose a novelAutoencoder-basedNetworkEmbeddingAlgorithm (AENEA). AENEA is mainly divided into three steps. First, the...