# Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks

## TL;DR

This paper explores how generative adversarial networks can create synthetic images to augment data, thereby enhancing the accuracy of object state classification in robotics and computer vision tasks.

## Contribution

It demonstrates the effectiveness of using GAN-generated synthetic images for data augmentation to improve classification accuracy in object recognition tasks.

## Key findings

- Synthetic data improves classification accuracy
- GAN-based augmentation enhances model robustness
- Application to robotic object state recognition

## Abstract

Object detection and recognition has been an ongoing research topic for a long time in the field of computer vision. Even in robotics, detecting the state of an object by a robot still remains a challenging task. Also, collecting data for each possible state is also not feasible. In this literature, we use a deep convolutional neural network with SVM as a classifier to help with recognizing the state of a cooking object. We also study how a generative adversarial network can be used for synthetic data augmentation and improving the classification accuracy. The main motivation behind this work is to estimate how well a robot could recognize the current state of an object