Question about autoencoders • /r/MachineLearning

Tl;dr - has this idea about neural network architecture, similar to autoencoders, been developed already? Correct me if I'm wrong about anything, but this is my understanding so far: Autoencoders purposefully have an information bottleneck in the middle, and this bottleneck is what forces the network to learn high level representations of the input data. Otherwise, without the bottleneck, the network may discover that the optimal connection is one that is roughly equivalent to directly mapping each input to its corresponding output. However, because that bottleneck exists, it is very unlikely that an autoencoder could perform a perfect reconstruction of the input. Contrast this with other encoding, like DCT as used by JPEG images.