Real life applications of Generative Adversarial neural networks
Generative adversarial networks are a form of neural network that can produce new images that are identical to the given dataset from a given set of images, but different individually. They are made up of two models of neural networks, a generator and a discriminator. They are used in industries where computer vision plays a significant role, such as photography, image processing, and games, and many more, as generative adversarial networks learn to identify and differentiate images. In this post we would be exploring a few applications of Generative Adversarial Neural Networks.
- Cyber security:
Many organizations are implementing advanced security strategies to avoid the leaking and exploitation of classified information. Yet, in order to access and manipulate user data, hackers come up with new approaches. One such method used by hackers is an adversarial attack. Through applying malicious data to them, hackers exploit images. This tricks the neural network itself and defeats the algorithm's planned operation. This, in turn, will result in the exposure and compromise of unauthorized data.
To recognize such instances of fraud, generative adversarial networks can be trained. They can be used to make more stable deep learning models. The neural network can be equipped to detect any malicious data that hackers could apply to photos.
One of the main winners of incorporating artificial intelligence, neural networks, and generative adversarial networks is expected to be the health and pharmaceutical sector. In medical tumor identification, GANs can be used. Through matching images with a list of images of healthy organs, the neural network may be used to classify tumors.
When compared to the data collection images, the neural network can find abnormalities in the patient's scans and images. The use of generative adversarial networks helps in the identification of cancerous tumors becoming more rapid and precise. For patients as well as doctors, it helps save money. However, generative adversarial networks can theoretically help save human lives, most notably.
For data conversion from images, generative adversarial networks may be used. For image-to-image translations, semantic image-to-photo translations, and translations of text-to-image, GANs can be used. Image to image translations: GANs can be used in image-to-image translations for translation activities such as:
- Editing details from day to night and vice versa.
- Transforming black and white photographs to color.
- Converting satellite photographs to Google Maps.
- Changing sketches to color photographs.
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Ashpreet Kaur - Jul 2, 2021
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