# Image Recognition for Garbage Classification Based on Pixel Distribution Learning

arXiv: https://arxiv.org/abs/2409.03913 · HTML: https://mlsanity.com/paper/2409.03913/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Further advantages of data augmentation on convolutional neural networks](https://mlsanity.com/paper/1906.11052/) (2019)
- [Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module](https://mlsanity.com/paper/2309.00752/) (2023)

## Abstract (arXiv)

The exponential growth in waste production due to rapid economic and industrial development necessitates efficient waste management strategies to mitigate environmental pollution and resource depletion. Leveraging advancements in computer vision, this study proposes a novel approach inspired by pixel distribution learning techniques to enhance automated garbage classification. The method aims to address limitations of conventional convolutional neural network (CNN)-based approaches, including computational complexity and vulnerability to image variations. We will conduct experiments using the Kaggle Garbage Classification dataset, comparing our approach with existing models to demonstrate the strength and efficiency of pixel distribution learning in automated garbage classification technologies.