OCFormer: One-Class Transformer Network for Image Classification

Prerana Mukherjee; Chandan Kumar Roy; Swalpa Kumar Roy

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arXiv · 2204.11449

OCFormer: One-Class Transformer Network for Image Classification

Prerana Mukherjee·Chandan Kumar Roy·Swalpa Kumar Roy

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Abstract

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.

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BibTeX RIS

Prerana Mukherjee, Chandan Kumar Roy, Swalpa Kumar Roy. 2022-04-25. OCFormer: One-Class Transformer Network for Image Classification. https://arxiv.org/abs/2204.11449

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