论文标题
基于变压器的维度降低
Transformer-based dimensionality reduction
论文作者
论文摘要
最近，变压器很受欢迎，在机器学习（ML），自然语言处理（NLP）和计算机视觉（CV）等领域中起着重要作用。在本文中，基于Vision Transformer（VIT）模型，提出了一种新的维度降低（DR）模型，提出了一种命名，命名为Transformer-DR。根据数据可视化，图像重建和面部识别，研究了降低降低后变形金刚的表示能力，并将其与某些代表性的DR方法进行比较，以了解Transferter-DR和现有DR方法之间的差异。实验结果表明，变压器-DR是一种有效的维度降低方法。
Recently, Transformer is much popular and plays an important role in the fields of Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV), etc. In this paper, based on the Vision Transformer (ViT) model, a new dimensionality reduction (DR) model is proposed, named Transformer-DR. From data visualization, image reconstruction and face recognition, the representation ability of Transformer-DR after dimensionality reduction is studied, and it is compared with some representative DR methods to understand the difference between Transformer-DR and existing DR methods. The experimental results show that Transformer-DR is an effective dimensionality reduction method.
