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.

Dec 6, 2020 · In this tutorial, you will discover how to develop and evaluate an autoencoder for classification predictive modeling. After completing this tutorial, you will know: An autoencoder is a neural network model that can be used to learn a compressed representation of raw data.

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.

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.

Apr 24, 2025 · Convolutional Autoencoders have shown impressive results in a variety of computer vision tasks, including image compression, denoising, and feature extraction. They have also been used in various applications such as image …

Aug 30, 2023 · In this study, we proposed a Time-Feature Attention (TFA)-based Convolutional Auto-Encoder (TFA-CAE) network model to extract essential flight features from QAR data.

Apr 27, 2025 · 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 …

To get the convolved features, for every 8x8 region of the 96x96 image, that is, the 8x8 regions starting at (1, 1), (1, 2), \ldots (89, 89), you would extract the 8x8 patch, and run it through your trained sparse autoencoder to get the feature activations. This would result in 100 sets 89x89 convolved features.

May 8, 2023 · In the encoder, I can replace traditional convolutional layers with Inverted Residual Blocks, reducing the computational complexity and memory usage while maintaining performance.

Oct 9, 2023 · Autoencoders are a class of artificial neural networks used in tasks like data compression and reconstruction. In this article, we will delve into the world of autoencoders, explore their architecture, and discuss autoencoders as a powerful tool for feature selection.