BioGAN: An unpaired GAN-based image to image translation model for microbiological images

Understand

A diversified dataset is crucial for training a well-generalized supervised computer vision algorithm.

• However, in the field of microbiology, generation and annotation of a diverse dataset including field-taken images are time consuming, costly, and in some cases impossible.
• Image to image translation frameworks allow us to diversify the dataset by transferring images from one domain to another.
• However, most existing image translation techniques require a paired dataset (original image and its corresponding image in the target domain), which poses a significant challenge in collecting such datasets.