Generative Adversarial Network or what is more commonly known as GAN is typically used for generative modeling. This system basically makes use of a model in order to provide New examples that arguably come from an existing sample. A simple example can be the generation of a new photograph that is much like the prototype but different from other photographs that are already in existence. This network generally makes the use of 2 distinguished neural networks. One such network is known as the generative network while the other one is called the discriminative network.
Widely Adopted GAN
GAN has been widely adopted and can be found in the following applications:
1. Generate Human Photographs
Some of the biggest examples of GAN generated images are real photographs of human beings that are based on a particular prototype.
2. Generate Cartoon Characters
GAN is now capable of generating cartoon characters. This was first put to test in the generation of anime Characters from an example.
3. Generate Daily Life Photograph
GAN can generate realistic images from our day-to-day lives. The use of comprehensive technology has led to the generation of synthetic photographs with the help of GAN.
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4. Editing Photograph
The GAN Applications can be used for a wide plethora of photo editing that has been considered very important in today’s day and age.
5. Translation of Images
GAN has been used to successfully translate images from one form to another. This may include the Translation of the photograph into a painting or even transforming a photo taken in daylight into one taken at night time.
6. Generation of emoji
The GAN method has been used for transforming pictures into emojis. This has been an extremely popular concept on social media platforms.
7. Enhancing Picture Quality
GAN has been successfully used for making picture quality better. With this network, you will be able to get pictures in high resolution even taken with a regular phone camera.
GAN has been used in a number of ways since its inception. From picture generation to transformation, this network is capable of its own. It has also been credited with other applications including photo blending and face aging. This is why the GAN method has become so popular.
