- How are autoencoders different from a conventional feed forward neural network?
Autoencoders are neural networks that learn a compressed representation of the input, known as the latent representation. They are different from conventional feed forward neural networks because their structure consists of an encoder and a decoder component, which is not present in CNNs.
- What happens when the latent representation of the autoencoder is too small?
The size of the latent representation should be sufficiently small enough to represent a compressed representation of the input, and also be sufficiently large enough for the decoder to reconstruct the original image without too much loss.
- What are the input and output when training a denoising autoencoder?
The input to a denoising autoencoder should be a noisy image and the output should be a reference clean image. During...
