Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks

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.