# Triplet Permutation Method for Deep Learning of Single-Shot Person Re-Identification

arXiv: https://arxiv.org/abs/2003.08303 · HTML: https://mlsanity.com/paper/2003.08303/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Octuplet Loss: Make Face Recognition Robust to Image Resolution](https://mlsanity.com/paper/2207.06726/) (2022)

## Abstract (arXiv)

Solving Single-Shot Person Re-Identification (Re-Id) by training Deep Convolutional Neural Networks is a daunting challenge, due to the lack of training data, since only two images per person are available. This causes the overfitting of the models, leading to degenerated performance. This paper formulates the Triplet Permutation method to generate multiple training sets, from a certain re-id dataset. This is a novel strategy for feeding triplet networks, which reduces the overfitting of the Single-Shot Re-Id model. The improved performance has been demonstrated over one of the most challenging Re-Id datasets, PRID2011, proving the effectiveness of the method.