Autoencoder Training Algorithm: Difference between revisions

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(Created page with "An Autoencoder Training Algorithm is a supervised learning algorithm that can be implemented by an auto-encoding system (that can solve an auto-encoding training task to train an auto-encoder). * <B>Example(s):</B> ** a Denoising Autoencoding Algorithm. ** a Variational Autoencoding Algorithm. ** ... * <B>See:</B> Stacked Auto-Encoding Algorithm. ---- ---- __NOTOC__ Category:Concept")
 
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An [[Autoencoder Training Algorithm]] is a [[supervised learning algorithm]] that can be implemented by an [[auto-encoding system]] (that can solve an [[auto-encoding training task]] to train an [[auto-encoder]]).
An [[Autoencoder Training Algorithm]] is a [[supervised learning algorithm]] that can be implemented by an [[auto-encoding system]] (that can solve an [[auto-encoding training task]] to train an [[auto-encoder]]).
* <B>Context:</B>
** It can use backpropagation and gradient descent to update the network weights during training.
** It can converge when the reconstruction error stabilizes or reaches a predefined threshold, indicating that the autoencoder has learned a suitable representation.
** ...
* <B>Example(s):</B>
* <B>Example(s):</B>
** a [[Denoising Autoencoding Algorithm]].
** a [[Denoising Autoencoding Algorithm]]: An algorithm that trains an autoencoder to reconstruct clean data from noisy inputs, thus making the model more robust to noise.
** a [[Variational Autoencoding Algorithm]].
** a [[Variational Autoencoding Algorithm]]: An algorithm that trains a [[Variational Autoencoder (VAE)]], incorporating probabilistic elements into the latent space to enable generative modeling.
** a [[Sparse Autoencoding Algorithm]]: An algorithm that applies sparsity constraints on the hidden layers to encourage the autoencoder to learn a more efficient and compressed representation of the data.
** a [[Stacked Autoencoding Algorithm]]: An algorithm where multiple autoencoders are trained in layers, each learning to encode the features output by the previous layer, resulting in deep feature learning.
** a [[Contractive Autoencoding Algorithm]]: An algorithm that applies a penalty on the sensitivity of the encoded features to small variations in the input, making the model more robust and focused on relevant features.
** a [[Convolutional Autoencoding Algorithm]]: An algorithm that applies convolutional layers to learn spatial hierarchies in image data, often used in image-based autoencoders.
** ...
** ...
* <B>See:</B> [[Stacked Auto-Encoding Algorithm]].
* <B>Counter-Example(s):</B>
** a [[Principal Components Analysis (PCA) Algorithm]]: A linear dimensionality reduction technique that transforms data into principal components, but it does not use neural networks or involve reconstruction tasks.
** a [[Generative Adversarial Network (GAN) Training Algorithm]]: An unsupervised learning algorithm that involves training two neural networks—a generator and a discriminator—in an adversarial setting to generate realistic data, without reconstructing input data as autoencoders do.
* <B>See:</B> [[Stacked Auto-Encoding Algorithm]], [[Autoencoder Training System]], [[Feature Learning Algorithm]], [[Dimensionality Reduction Algorithm]].


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Revision as of 07:35, 10 September 2024

An Autoencoder Training Algorithm is a supervised learning algorithm that can be implemented by an auto-encoding system (that can solve an auto-encoding training task to train an auto-encoder).