论文标题
Ocformer：用于图像分类的一级变压器网络
OCFormer: One-Class Transformer Network for Image Classification
论文作者
论文摘要
我们提出了一个基于视觉变压器（VIT）的新型深度学习框架，以进行一级分类。核心思想是将以零为中心的高斯噪声用作伪阴性类作为潜在空间表示形式，然后使用最佳损耗函数训练网络。在先前的工作中，已经做出了巨大的努力，以使用各种损失功能来学习良好的表示，从而确保歧视性和紧凑的特性。拟议的单级视觉变压器（OCFORMER）在CIFAR-10，CIFAR-100，时尚摄影师和Celeba Eyeglasses数据集上进行了详尽的实验。我们的方法表明，基于CNN的单级分类器方法，已显示出显着改善。
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.
