Moments of epiphany tend to come in the unlikeliest of circumstances.
Goodfellows friends were discussing how to use AI to create photos that looked realistic.
That same night, he coded and tested his idea and it worked.
Since then, GAN has sparked many new innovations in the domain of artificial intelligence.
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DNNs rely on large sets of labeled data to perform their functions.
It will then be able to find cats in pictures it has never seen before.
The same logic is behind facial recognition and cancer diagnosis algorithms.
But deep neural networks suffer fromsevere limitations.
Prominent among them is the heavy reliance on quality data.
The training data of a deep learning app often determines the scope and limit of its functionality.
The real limits of neural networks manifest themselves when you use them to generate new data.
Deep learning is very efficient at classifying things but not so good at creating them.
Source: Google
This is where GANs come into play.
How does GAN work?
Ian Goodfellows Generative Adversarial connection technique proposes that you use two neural networks to create and refine new data.
The first connection, thegenerator, generates new data.
The process is, simply put, the reverse of neural networks classification function.
The second web link, thediscriminator, is a classifier DNN.
It rates the quality of the results of the generator on a scale of 0 to 1.
If the score is too low, the generator corrects the data and resubmits it to the discriminator.
Not all the photos the AI creates are prefect, but some of them look impressively real.
The applications of GAN
Generative adversarial networks have already shown their worth in creating and modifying imagery.
There are many practical applications for GAN.
For instance, it can be used to create random interior designs to give decorators fresh ideas.
But the applications of GAN stretch beyond creating realistic-looking photos, videos and works of art.
It can help speed research and progress in several areas where AI is involved.
GAN can be crucial in areas where access to quality data is difficult or expensive.
GANs are perfect for the task, as it happens.)
First, GANs show a form of pseudo-imagination.
Depending on the task theyre performing, GANs still need a wealth of training data to get started.
This means that areas where data is non-present wont be able to use GAN.
GANs cant invent totally new things.
it’s possible for you to only expect them to combine what they already know in new ways.
Also, at this stage, handling GANs is still complicated.
If theres no balance between the generator and the discriminator, results can quickly get weird.
Engineers must constantly optimize the generator and discriminator networks sequentially to avoid these effects.
Weve already seen this happen to deep learning.
GANs can also be used to find weaknesses in other AI algorithms.
GAN can also inflict real harm in areas where AI coincides with the physical world.
For instance, it can help find patterns that will fool self-driving cars intomissing obstacles or misreading street signs.
Researchers have already found ways to fool self-driving cars AI algorithms to miss street signs.
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