Point cloudRegistrationSearching corresponding features between two consecutive point cloud sequences is a key step in frame-to-frame matching. Utilizing multi-resolution strategy and voxel model which reserves the original shape of input data, we propose a fully unsupervised Convolutional Auto-Encoder (...
The convolution operator allows filtering an input signal in order to extract some part of its content. Autoencoders in their traditional formulation do not take into account the fact that a signal can be seen as a sum of other signals. Convolutional Autoencoders, instead, use the convolution...
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event...
With tiny-cnn is is possible to auto encode a convolutional layer? If so is there an example? Member nyanp commented Nov 8, 2015 @micahpearlman Currently tiny-cnn doesn't support convolutional auto encoder, because of lack of "unpooling" layer. Contributor bhack commented Jun 20, 2016 ...
the latent detection performance and the VAE is used as a core element in the encoder-decoder-encode (EDE) pipeline. To the best of our knowledge, this is the first study suggesting a mixture of CVAEs-based models for AD. The performance of the MEx-CVAE with EDE pipeline which we names...
Deep Residual UNET U-shaped -encode and decode network VGG-16UNET: Visual Geometry Group 16 DENSENET: Densely Connected Convolutional Networks ISIC: International Skin Imaging Collaboration ReLU: Rectified Linear Unit 2D: 2-Dimensional 3D: 3-Dimensional ...
Another popular method is to train auto-encoders (convolutionally, stacked (Vincent et al., 2010), separating the what and where components of the code (Zhao et al., 2015), ladder structures (Rasmus et al., 2015)) that encode an image into a compact code, and decode the code to rec...
Long non-coding RNAs (lncRNAs) are a type of non-coding RNA with the length of more than 200 nucleotides, which cannot encode proteins [1]. The lncRNAs play important roles in many human biologic processes, such as oncogenesis, gene regulation, protein translation, expression, tissue developmen...
Trainauto-encoders(convolutionally, stacked (Vincent et al., 2010), separating the what and where components of the code (Zhao et al., 2015), ladder structures (Rasmus et al., 2015)) that encode an image into a compact code, and decode the code to reconstruct the image as accurately ...
14.2.3 Convolutional-autoencoder model A classical autoencoder structure is based on unsupervised learning techniques that aim to recompose the input data by minimizing the reconstitution error [54]. If an input dataset is considered to be {a1, a2,…, am} for ak∈ ℝm, the purpose of au...