Some places have clear sky and greenery most of the time, and rarely experience snow or rain. We expect the self- driving cars to operate consistently whether rain or snow.
This AI framework lets machines imagine: How a place looks l ike when i t’ s raining or snowing, by looking at the image taken during normal ( clear / sunny) weather and vice-versa.
Cycle GAN is a technique for training unsupervised image translation models via the GAN architecture using unpaired collections of images from two different domains. Cycle GAN uses a cycle consistency loss to enable training without the need for paired data.
Cycle GAN Applications: Season translation, object transfiguration, style transfer, and generating photos from paintings.
The Cycle GAN is an extension of the GAN architecture that involves the simultaneous training of two generator models and two discriminator models.
One generator takes images from the first domain as input and outputs images for the second domain, and the other generator takes images from the second domain as input and generates images for the f irst domain. Discriminator models are then used to determine how plausible the generated images are and update the generator models accordingly.
Preparation of the training Dataset: