Nov 11, 2023 · Reconstruction Loss: When training an autoencoder, choosing the appropriate reconstruction loss is essential. Common options include Mean Squared Error (MSE) Loss …

Apr 14, 2025 · Autoencoders are one of the most popular self-supervised learning approaches in the context of deep generative models. The basic idea si to map an arbitrary input x x to a …

Mar 1, 2025 · Autoencoders aim to minimize reconstruction error which is the difference between the input and the reconstructed output. They use loss functions such as Mean Squared Error …

The reconstruction loss for a VAE (see, for example equation 20.77 in The Deep Learning Book) is often written as $-\mathbb{E}_{z\sim{q(z | x)}} log(p_{model}(x | z))$, where $z$ represents …

Learn about most common types of autoencoders and their applications in machine learning. Autoencoders have emerged as one of the technologies and techniques that enable computer …

Jun 30, 2022 · VAEs train by maximizing the evidence lower bound (ELBO) on the marginal log-likelihood. In practice, optimize the single sample Monte Carlo estimate of this expectation: …

Let's examine the two most prevalent reconstruction loss functions used in autoencoders: Mean Squared Error (MSE) and Binary Cross-Entropy (BCE). Mean Squared Error, also known as …

Aug 16, 2024 · For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent …

Jul 24, 2022 · Loss functions plays a crucial role when image reconstruction is performed using a convolutional autoencoder. In this study to analyze the performance of various loss functions …

Aug 31, 2023 · By using a neural network, the autoencoder is able to learn how to decompose data (in our case, images) into fairly small bits of data, and then using that representation, …