Jan 1, 2018 · In this paper, we present a Deep Learning method for semi-supervised feature extraction based on Convolutional Autoencoders that is able to overcome the aforementioned problems. The proposed method is tested on a real dataset for Etch rate estimation.

Instead of using various complex perception algorithms, and/or manually choosing task-specific data features, this unsupervised feature extraction method allows simultaneous online deployment of multiple simple perception algorithms on a common set of black-box features.

In this paper, we propose a deep learning-based YLP feature extraction that jointly captures daily and seasonal variations. By leveraging convolutional autoencoder (CAE), YLPs in 8,640-dimensional space are compressed to 100-dimensional vectors.

We present a novel convolutional auto-encoder (CAE) for unsupervised feature learning. A stack of CAEs forms a convolutional neural network (CNN). Each CAE is trained using conventional on-line gradient descent without additional regularization terms.

May 1, 2022 · In this paper, we design a fully unsupervised convolutional auto-encoder combined with multi-resolution for feature extraction from multi-beam LiDAR point cloud. Multiple resolution helps describe features in macro and micro view meantime, and CAE can further refine the complicated multi-dimensional features to a concise but rich enough one.

We present a novel convolutional auto-encoder (CAE) for unsupervised feature learning. A stack of CAEs forms a convolutional neural network (CNN). Each CAE is trained using conventional on-line gradient descent without additional regularization terms.

Abstract: This paper presents Autoencoder using Convolutional Neural Network for feature extraction in the Content-based Image Retrieval. Two type of layers are in the convolutional autoencoder architecture, they are encoder and decoder layer.

5 days ago · Convolutional Neural Networks (ConvNets or CNNs) are powerful tools for automatically extracting meaningful patterns from images. Instead of manually designing features like edges, corners, or textures, CNNs learn to detect …

12 hours ago · The architecture of the CAE model is based on a convolutional autoencoder (CAE) proposed in Ref. . It consists of an encoder with convolutional layers that compresses the image to a lower dimensional latent space, and a decoder with deconvolutional layers that reconstructs the image from the latent space to the original input dimensionality.

FeXT AutoEncoder is a project centered around the implementation, training and evaluation of a Convolutional AutoEncoder (CAE) model specifically designed for efficient image feature extraction.