# One-Shot learning based classification for segregation of plastic waste

arXiv: https://arxiv.org/abs/2009.13953 · HTML: https://mlsanity.com/paper/2009.13953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

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

- [Detailed Evaluation of Modern Machine Learning Approaches for Optic Plastics Sorting](https://mlsanity.com/paper/2505.16513/) (2025)

## Abstract (arXiv)

The problem of segregating recyclable waste is fairly daunting for many countries. This article presents an approach for image based classification of plastic waste using one-shot learning techniques. The proposed approach exploits discriminative features generated via the siamese and triplet loss convolutional neural networks to help differentiate between 5 types of plastic waste based on their resin codes. The approach achieves an accuracy of 99.74% on the WaDaBa Database