that's just an or encoder but if you add some level of noise it's possible that you learn more useful features because you're learning to separate noise from the actual signal right and if you had too much noise then it may actually be a really hard task because the signal-to-noise ratio will you really low so this is the general computation graph of the denoising auto-encoder where x tilde refers to the noise version and F data basically at the ground truth x and you're trying to figure out how to reconstruct that back