i am just thinking about training a neural network which uses data of only one single label.
For example: Assuming i have many images which contain a dog. Now i want to teach the network how a dog looks like, without images containing no dogs.
More formal: I want to check for availability of one label, so in theory, i need 2 datasets: Dog and NoDog
The question i am raising to myself now is: How would i define the NoDog-class? I can throw in anything, some random noise or just any unlabelled data which does not contain a dog? But how do i make sure that i sample the "no-dog"- space correctly? Which features does a "no-dog" image have? The "no-dog" space is huge, it is extremely difficult to describe it, somehow.
I had different ideas:
-Use the same dog-images, but cut out the dogs and fill it with noise
-Train a gan and use the discriminator for the binary classification
-Use tons of unlabelled data
Does anyone know how to tackle this problem in a systematic manner? Thank you