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Can GAN use for data augmentation?
4 Answers. Yes, GAN can be used to “hallucinate” additional data as a form of data augmentation.
How do you solve an imbalanced image dataset?
One of the basic approaches to deal with the imbalanced datasets is to do data augmentation and re-sampling. There are two types of re-sampling such as under-sampling when we removing the data from the majority class and over-sampling when we adding repetitive data to the minority class.
What is the application of GAN?
For example, GAN can be used for the automatic generation of facial images for animes and cartoons. The generative adversarial network is trained on a specialized dataset such as anime character designs. The GAN generates new characters by analyzing the dataset of images provided.
How are Gans used to augment datasets?
An even more recent development is the use of GANs to generate images that can be used to augment datasets. Over the past few months, I’ve been working on a project that uses CycleGANs (more specific info about CycleGAN in my previous blogpost) to generate synthetic data for a colorectal polyps dataset.
How to conquer class imbalanced dataset issues using GANs?
Construct different mini-batches for real and fake, i.e. each mini-batch needs to contain only all real images or all generated images. Learning rates >> Use different learning rates for discriminator (1e-3) and generator (1e-4). Use Adam optimizer for both. Performance hack >> Train discriminator twice and generator once.
How are Gans used to generate medical images?
Use GANs to generate images of the rare diseases so that you don’t have that problem anymore. More specifically, CycleGAN might be better for medical images because information from one class might be useful to create synthetic data in another class.
How is data augmentation used in real world?
However, collecting such amount of data may be hard and expensive, especially for real-world problems. To deal with it, we can apply a well-known technique called Data Augmentation, which uses different random transformations to increase the dataset and improve variance.