Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks
This work addresses the challenge of data collection for object state recognition in robotics, but appears incremental as it combines existing methods without introducing a new paradigm.
The paper tackled the problem of recognizing object states in robotics by using a deep convolutional neural network with SVM for classification and studied synthetic data augmentation with generative adversarial networks to improve accuracy, but did not report concrete numbers for the results.
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