Can I use a GAN to increase my Dataset used for Image detection?

I am currently working on a machine learning project where I use the YOLO Algorithm to detect an object inside of a picture or video. The problem I face is that the specific image set (side-scan sonar) that I am working with is mostly classified, thus there is not a wide range of images available to the public to be used for training. Would I be able to implement a GAN to produce a larger data-set of side-scan sonar imagines to be used for training an image detection Algorithm? I understand that there may be image distortion from using a GAN, but for the purpose of having a larger training set would this be possible?

Topic gan tensorflow deep-learning machine-learning

Category Data Science

1
answered at

You don't say what the object is. If you're using YOLO (which is suited to flat things/surfaces), then is creating similar objects in CGI feasible?

Definitely a learning curve to CGI, but if the object has a simple appearance, CGI might be adequate. It can allow you to randomize in many ways, creating a valid data set.

1
answered at

In short: no

If you don't have enough images now then you almost certainly dont have enough to successfully train a GAN (it takes more than you think). If you can't train a GAN to reproduce good images, then you don't have images that are going to give you high accuracy for your YOLO effort. The most likely scenario is that you would end up with an algorithm that is really, really good at detecting GAN images that looking nothing like your side scan sonar validation data :)

About

Geeks Mental is a community that publishes articles and tutorials about Web, Android, Data Science, new techniques and Linux security.