Convolutional autoencoder. Explore the latent space, visualize the embeddings, and reconstruct images with the trained model. Autoencoders are neural networks that learn efficient codings of unlabeled data, and can be used for unsupervised learning, feature extraction, and generative models. Here are some of the most commonly used types of autoencoders: A Convolutional Autoencoder (CAE) is a type of autoencoder designed specifically for image data. Mar 15, 2025 · This paper provides a comprehensive review of autoencoder architectures, from their inception and fundamental concepts to advanced implementations such as adversarial autoencoders, convolutional autoencoders, and variational autoencoders, examining their operational mechanisms, mathematical foundations, typical applications, and their role in . org Jun 23, 2024 · Learn how to build and train autoencoders with PyTorch, a deep learning framework. Jun 16, 2024 · Part 2: Convolutional Autoencoder (CAE) There are several types of autoencoders, each designed for a specific type of input data or task. It consists of an encoder that reduces the image to a compact feature representation and a decoder that restores the image from this compressed form. Aug 5, 2025 · A Convolutional Autoencoder (CAE) is a type of neural network that learns to compress and reconstruct images using convolutional layers. CAEs are widely used for image denoising, compression and feature extraction due to their ability to preserve Apr 27, 2025 · Autoencoder Architecture A custom convolutional autoencoder architecture is defined for the purpose of this article, as illustrated below. Here, the encoder and decoder are built using convolutional neural networks (CNNs) (instead of Jul 17, 2023 · Learn how to train and use a convolutional autoencoder with PyTorch on the Fashion-MNIST dataset. See full list on tensorflow. This architecture is designed to work with the CIFAR-10 dataset as its encoder takes in 32 x 32 pixel images with three channels and processes them until 64 8 x 8 feature maps are produced. jfczlh tun erctsadh wqfmt jtdfxh kokmzj eapt pkrxzk qofby zcfxm