mlsanityData to 14 Oct 2026

OCFormer: One-Class Transformer Network for Image Classification

arXiv 2204.11449 ↗First posted 25 Apr 2022cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 25 Apr 2022.

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References · 23

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11 more
  1. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  2. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  3. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
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  10. 2019One-Class Convolutional Neural Networkcited by 37
  11. 2019Attribute Restoration Framework for Anomaly Detectioncited by 16

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