Train a GAN on the images of class A. The generator will draw samples from the distribution outlined by the images in class A. The discriminator measures the distance between given sample and this distribution. So once you finish training on class A, the critic will tell you whether or not a given image belongs to class A.

An alternative approach is to do self-supervised representation learning (like BYOL) and compare the projection distance between a pair of A and B images.