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...
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...
We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By ...
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 ...
lung cancer pathological image analyzer based on convolutional autoencoder extern_params.py :define parameters and global variables tensorflow_ae_base.py :basic functions for CNN tensorflow_ae_stage1.py :define network of the first stage tensorflow_train_stage1.py :train network of the first stage...
2012) to learn powerful image representations. 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...
Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling
ConvNets usually accept input as image and encode features into the network which decreases the number of parameters. In real-world applications, the performance of the convolutional neural network is much better than that of multi-layer perceptrons. There are two issues found in multi-layer ...
In particular, the the hierarchical nature of a stacked autoencoder allows us to encode different types of (progressively more complex) features in each hidden layer, similarly to CNNs. Stanford's UFLDL has a great explanation of this: The first layer of a stacked autoencoder ...