Regular AE’s job is to throw away the information outside some low-dimensional manifold, sparse ~linear AE’s job is to throw away the information not represented by sparse dictionary codes. (Also a low-dimensional manifold, I guess, just made from a different prior.)
If an AE is reconstructing poorly, that means it was throwing away a lot of information. How important that information is seems like a question about which manifold the underlying network “really” generalizes according to. And also what counts as an anomaly / what kinds of outliers you’re even trying to detect.
Regular AE’s job is to throw away the information outside some low-dimensional manifold, sparse ~linear AE’s job is to throw away the information not represented by sparse dictionary codes. (Also a low-dimensional manifold, I guess, just made from a different prior.)
If an AE is reconstructing poorly, that means it was throwing away a lot of information. How important that information is seems like a question about which manifold the underlying network “really” generalizes according to. And also what counts as an anomaly / what kinds of outliers you’re even trying to detect.