Aug 16, 2024 · This notebook demonstrates how to train a Variational Autoencoder (VAE) (1, 2) on the MNIST dataset. A VAE is a probabilistic take on the autoencoder, a model which takes high dimensional input data and compresses it into a smaller representation.

Mar 1, 2021 · This example demonstrates how to implement a deep convolutional autoencoder for image denoising, mapping noisy digits images from the MNIST dataset to clean digits images. This implementation is based on an original blog post titled Building Autoencoders in Keras by François Chollet.

Jul 17, 2023 · In this tutorial, we will walk you through training a convolutional autoencoder utilizing the widely used Fashion-MNIST dataset. We will then explore different testing situations (e.g., visualizing the latent space, uniform sampling of data points from this latent space, and recreating images using these sampled points).

Feb 24, 2024 · Convolutional autoencoders leverage convolutional layers to excel in image-related tasks, capturing spatial relationships effectively.

Jul 31, 2023 · Define the Convolutional Autoencoder architecture by creating an Autoencoder class that contains an encoder and decoder, each with convolutional and pooling layers. Initialize the autoencoder model and move it to the GPU if available using the to() method.

Feb 7, 2023 · The autoencoder is trained by minimizing the mean squared error between the reconstructed image and the original image. The script starts by loading the MNIST dataset and normalizing the pixel...

Jun 28, 2021 · In this series, I want to start from the simplest topics to the more advanced ones. The autoencoder is an unsupervised deep learning algorithm that learns encoded representations of the input...

Dec 14, 2020 · Train our convolutional variational autoencoder neural network on the MNIST dataset for 100 epochs. Save the reconstructions and loss plots. Analyze the results.

Define an autoencoder with two Dense layers: an encoder, which compresses the images into a 64 dimensional latent vector, and a decoder, that reconstructs the original image from the latent...

We'll train an autoencoder with MNIST images by flattening them into 784 length vectors. The images from this dataset are already normalized such that the values are between 0 and 1. Let's start by building a simple autoencoder. The encoder and decoder should be …