Image regression problem I've tried a number of experiments with machine learning. From trying to use GANs to upscale images to playing with auto-encoders.
There is one problem that haunts me and always ends up ruining my experiments.
My networks always seem to &quot;learn&quot; to ignore the input and always produce the same image output. This output is often an interesting blend of the input images.
It seems that it's learning to cheat the test and produce the same image regardless of the input.
Here are some examples:


This has happened in both my GAN and Auto-encoder projects.
How do I avoid this sort of thing happening?
Couple of Things straight up:

Inject some noise in the process. When the gan or autoencoder learns that there is some noise it will start to generalise better

Use weaker architectures. (Analogy to weak learners, you cant build random forest wit hstrong trees). Basically allowing for your Architecture to be weak enough to be able to generalise and not learn everything by heart, i.e. map Input to Output.