You'd get the input image. A NN requires negatives. If you train it to accept "everything" as a positive, then you just get the original back. You need to mo

You'd get the input image. A NN requires negatives. If you train it to accept "everything" as a positive, then you just get the original back. You need to more precisely define your negatives, to get anything interesting back.