# Deep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data

## TL;DR

This paper introduces an improved deep convolutional GAN approach for food recognition that effectively utilizes partially labeled data, overcoming the need for extensive labeled datasets and outperforming current state-of-the-art methods.

## Contribution

The paper develops a robust deep convolutional GAN architecture tailored for food recognition that operates efficiently with partially labeled data, enhancing classification accuracy.

## Key findings

- Outperforms existing methods on benchmark datasets
- Effective with limited labeled training data
- Demonstrates robustness and efficiency in food image classification

## Abstract

Traditional machine learning algorithms using hand-crafted feature extraction techniques (such as local binary pattern) have limited accuracy because of high variation in images of the same class (or intra-class variation) for food recognition task. In recent works, convolutional neural networks (CNN) have been applied to this task with better results than all previously reported methods. However, they perform best when trained with large amount of annotated (labeled) food images. This is problematic when obtained in large volume, because they are expensive, laborious and impractical. Our work aims at developing an efficient deep CNN learning-based method for food recognition alleviating these limitations by using partially labeled training data on generative adversarial networks (GANs). We make new enhancements to the unsupervised training architecture introduced by Goodfellow et al. (2014), which was originally aimed at generating new data by sampling a dataset. In this work, we make modifications to deep convolutional GANs to make them robust and efficient for classifying food images. Experimental results on benchmarking datasets show the superiority of our proposed method as compared to the current-state-of-the-art methodologies even when trained with partially labeled training data.