Can a trained recognition model be used to generate a sample? Suppose we have trained a cat classification network. It takes in an image (as a vector) x and returns $\hat{y}\in(0,1)$. The loss function is the typical cross entropy function. Shouldn't it be possible to now perform gradient descent on the space of images to obtain an example of a &quot;cat picture&quot; that our network really thinks is a cat?
I expect that this process would give us something very ugly, and not actually cat-like, but I am just curious if this works.
Generative models can. Among others, you can use GAN, auto-encoders or VAE, ... As far as I know, discriminative approaches can not generate a sample.
Hope it helps.