This project implements an autoencoder to encode and decode the Olivetti Faces dataset. The autoencoder is a neural network designed to compress images into a reduced latent representation and ...

Unsupervised learning is used mainly to discover patterns ... networks that are trained on their inputs. Essentially, the autoencoder is a feed-forward network that acts as a codec, encoding ...

If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning models are developed.

In this paper, we present an unsupervised feature learning algorithm using the convolutional autoencoder for detection of plant diseases. The use of convolutional autoencoder has two main advantages.

Abstract: Dimensionality reduction is commonly used to preprocess high-dimensional data, which is an essential step in machine learning and data mining. An outstanding low-dimensional feature can ...

Create two empty folders named "DataArray" and "models" first. Use Main.py to train and test. t-SNE.py can be used to visualize the features.

This paper presents an unsupervised learning method to classify and label transients observed in the distribution grid. A Convolutional Variational Autoencoder (CVAE) was developed for this purpose.

Unsupervised machine learning learns from the data without human labelling ... To find anomalies, data passes through an autoencoder, a type of artificial neural network. According to Chepkwony, the ...