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 autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction, to generate lower-dimensional embeddings for subsequent use by …

Aug 16, 2024 · An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes …

An autoencoder neural network is an unsupervised learning algorithm that applies backpropagation, setting the target values to be equal to the inputs. I.e., it uses \textstyle …

Autoencoders are another family of unsupervised learning algorithms, in this case seeking to obtain insights about our data by learning compressed versions of the original data, or, in other …

May 24, 2021 · In this paper, we propose to use an end-to-end network for watermarking. We use a convolutional neural network (CNN) to control the embedding strength based on the image …

If the data is highly nonlinear, one could add more hidden layers to the network to have a deep autoencoder. Autoencoders belong to a class of learning algorithms known as unsupervised …

Apr 28, 2023 · In this paper, we propose to use an end-to-end network for watermarking. We use a convolutional neural network (CNN) to control the embedding strength based on the images’ …

Apr 18, 2023 · An autoencoder neural network is an Unsupervised Machine learning algorithm that applies backpropagation, setting the target values to be equal to the inputs. Autoencoders …

Dec 14, 2023 · Autoencoders are a special type of unsupervised feedforward neural network (no labels needed!). The main application of Autoencoders is to accurately capture the key …