Compiling...
In the previous note, we learned that the "Bottleneck" of an Auto-Encoder forces the network to learn the absolute core essence of an image (like its major shapes and edges) while discarding useless background details.
Researchers realized that this specific behavior—discarding useless details—made the Auto-Encoder the perfect tool for cleaning up damaged or noisy data.
