Turn a Doodle to a Realistic Looking Image with the AI Image Generator

Turn a Doodle to a Realistic Looking Image with the AI Image Generator

Does it happen with you too? You tend to see the same object or the same thing repeatedly and then the image is stuck in your head. Machine learning which ought to be the elementary platform for Artificial Intelligence(AI) goes with the same impression too. The main idea is to able to teach AI to take its own decisions when exposed to vast datasets.

Unpleasant Doodles:

Machine learning algorithms now are mainly emphasizing on training machines to identify designs/patterns. Let’s suppose a machine/system is exposed to a huge number feline images. The main idea behind, exposing the system to innumerable images of cats is to make them learn the characteristics of the animal, like how it looks. So, that next time when they are exposed to any such images these machines can identify it.
Though machine learning performs fantastically well while asked to identify images, they do goof up when asked to create one. Image Generator is the newest example of this and is shared as a part of the pix2pix project. Let’s try our hands on this Image Generating tool and have a look at the results.
Turn a doodle into realistic looking imageTurn a doodle into a realistic image
The results though are poor, when seen from a human perspective, but leaves no doubt that machine learning easily succeeded in turning a lifeless doodle into a picture which looks more realistic.

Machine Learning Future:

The pix2pix model working is based on working on a pair of images and picking out some of the features that it can identify using the machine learning system. The method is simple, simply draw an image and the machine learning algorithm will transform the same image into a spirited picture. Agreed, that so far the algorithm does not produce perfect realistic images, it however, manages to build one which can easily be recognized by humans.
Realistic images are generated by using the next gen machine learning methods known as generative adversarial networks(GAN). The machine majorly works at identifying whether the image generated looks real or fake. In case, if the image is considered fake the process is repeated all together until the generated images qualify the threshold for a real image.
There is no question that these image generating machines needs to be trained, but the transition from former times to what they are capable of doing now has considerably elevated and may be easily used to produce refined results from a raw stage in the near future.
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