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).
- Context:
- 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.
- ...
- Example(s):
- 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: 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.
- ...
- Counter-Example(s):
- 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.
- See: Stacked Auto-Encoding Algorithm, Autoencoder Training System, Feature Learning Algorithm, Dimensionality Reduction Algorithm.