A convolutional autoencoder is a neural network (a special case of an unsupervised learning model) that is trained to reproduce its input image in the output layer. An image is passed through an encoder, which is a ConvNet that produces a low-dimensional representation of the image. The decoder, which is another sample ConvNet, takes this compressed image and reconstructs the original image.
The encoder is used to compress the data and the decoder is used to reproduce the original image. Therefore, autoencoders may be used for data, compression. Compression logic is data-specific, meaning it is learned from data rather than predefined compression algorithms such as JPEG, MP3, and so on. Other applications of autoencoders can be image denoising (producing a cleaner image from a corrupted image), dimensionality reduction, and image search:
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