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Transformer-based dimensionality reduction

2022-10-15 13:24:43
Ruisheng Ran, Tianyu Gao, Bin Fang
arXiv_CV
             
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.08288

PDF

https://arxiv.org/pdf/2210.08288.pdf

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