Autoencoders are used for dimensionality reduction, or data compression, and image denoising. Dimensionality reduction, in turn, helps in improving runtime performance and consumes less memory. An image search can become highly efficient in low-dimension spaces.
Applications
An example of compression
The Network architecture comprises of an encoder network, which is a typical convolutional pyramid. Each convolutional layer is followed by a max-pooling layer; this reduces the dimensions of the layers.
The decoder converts the input from a sparse representation to a wide reconstructed image. A schematic of the network is shown here:
The encoder layer output image size is 4 x 4 x 8 = 128. The original image size was...
