  We propose a novel deep learning framework based on Vision Transformers (ViT)
for one-class classification. The core idea is to use zero-centered Gaussian
noise as a pseudo-negative class for latent space representation and then train
the network using the optimal loss function. In prior works, there have been
tremendous efforts to learn a good representation using varieties of loss
functions, which ensures both discriminative and compact properties. The
proposed one-class Vision Transformer (OCFormer) is exhaustively experimented
on CIFAR-10, CIFAR-100, Fashion-MNIST and CelebA eyeglasses datasets. Our
method has shown significant improvements over competing CNN based one-class
classifier approaches.
