How to use a dataset with only one category of data
The Model learns to match the weights as per the image and feedback from label data.
If you will feed a few Image classes as &quot;Not Cat&quot;, it will learn to classify similar features as &quot;Not Cat&quot;. But might fail for a new Class.
 e.g. if it is trained on &quot;Car/Furniture/Dog&quot; as &quot;Not Cat&quot;, then chances are high that a Wild Cat will be classified as Cat.
 Dumping all the Imagenet dataset will definitely provide quite a good variance to the &quot;Not Cat&quot; class and may work most of the time but that is not the appropriate solution for the problem.
Such type of problem will fall under One-Class-Classification.
Core idea is to use CNN to extract features then use some specialized models e.g. one-class SVM, Gaussian Mixtures, etc. to define a boundary for &quot;Cat&quot;

This problem, as defined by the one-class SVM approach, consists of identifying a sphere enclosing all (or the most) of the data. The classical strategy to solve the problem considers a simultaneous estimation of both the center and the radius of the sphere.

You may start with these links(In the specified order) -
Hackernoon blog
Arxiv
Researchgate
There are other approaches too i.e. based on Auto-encoder. Here, we try to put a Threshold on reconstruction error.
References-
Quora
SO
Keras blog
Also, may look here to check an idea which generated Random images for &quot;No Cat&quot; Class.Here