What is an autoencoder?
A system that learns to extract the most important information from the input.
autoencoder explained in plain English
A system that learns to extract the most important information from the input. Autoencoders are a combination of an encoder and decoder. Autoencoders rely on the following two-step process: 1. The encoder maps the input to a (typically) lossy lower-dimensional (intermediate) format. 2. The decoder builds a lossy version of the original input by mapping the lower-dimensional format to the original higher-dimensional input format. Autoencoders are trained end-to-end by having the decoder attempt to reconstruct the original input from the encoder's intermediate format as closely as possible. Because the intermediate format is smaller (lower-dimensional) than the original format, the autoencoder is forced to learn what information in the input is essential, and the output won't be perfectly identical to the input. For example: - If the input data is a graphic, the non-exact copy would be similar to the original graphic, but somewhat modified. Perhaps the non-exact copy removes noise from the original graphic or fills in some missing pixels. - If the input data is text, an autoencoder would generate new text that mimics (but is not identical to) the original text. See also variational autoencoders.
Example
Practitioners refer to autoencoder when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
People also read
- pooling
Reducing a matrix (or matrixes) created by an earlier convolutional layer to a smaller matrix.
- Attention
A mechanism that lets a model focus on the most relevant parts of its input when producing an output, weighting what matters most in context.
- auto-regressive model
A model that infers a prediction based on its own previous predictions.
- convolution
In mathematics, casually speaking, a mixture of two functions.
- convolutional filter
One of the two actors in a convolutional operation.
- convolutional layer
A layer of a deep neural network in which a convolutional filter passes along an input matrix.
- convolutional neural network
A neural network in which at least one layer is a convolutional layer.
- convolutional operation
The following two-step mathematical operation: 1.
- depth
The sum of the following in a neural network: - the number of hidden layers - the number of output layers, which is typically 1 - the number of any embedding layers For example, a neural network with five hidden layers and one output layer has a depth of 6.
- depthwise separable convolutional neural network
A convolutional neural network architecture based on Inception, but where Inception modules are replaced with depthwise separable convolutions.