Auto-encoder
时间: 2023-12-31 18:09:17 浏览: 35
An auto-encoder is a type of neural network that is trained to learn a compressed representation of input data. It consists of two parts: an encoder that compresses the input data into a lower-dimensional representation, and a decoder that reconstructs the original input data from the compressed representation.
During training, the auto-encoder learns to minimize the difference between the input data and the reconstructed output. This forces the network to learn a compressed representation that captures the most important features of the input data.
Auto-encoders have a wide range of applications, including image and speech recognition, anomaly detection, and data compression. They are particularly useful for unsupervised learning tasks, where the input data is not labeled and the network must learn to find patterns and structure in the data on its own.
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