Dueling Neural Networks, The GAN

Dueling Neural Networks, The GAN

Artificial Intelligence has so far been mainly the plaything of bog tech companies like Google, Microsoft, Amazon, as well as some startups. For many companies Artificial Intelligence systems are too expensive and it is difficult to implement fully.

AI is getting very good at identifying things: show it a million pictures, and it can tell you with uncanny accuracy which ones depict a pedestrian crossing a street. Now let’s explore it deeply and speak about the GAN.

So what is GAN? The main focus for Generative Adversarial Networks) is to generate data from scratch, mostly images but other domains including music have been done. But the scope of application is far bigger than this. GAN composes of two networks, the generator and the discriminator.

There is an extraordinary amount of data in the world and much of it is easily accessible – the difficult part is developing algorithms that can analyze and understand this abundance of data. Generative models are one of the most promising approaches towards this goal. Generative models have many short-term applications, but in the long-term they have the potential to learn the natural features of dataset, whether that’s categories or pixels, audio samples or something else entirely.

In a generative adversarial network, the generative network constructs results from input and shows them to the discriminative network. The discriminative network is supposed to distinguish between authentic and synthetic results given by the generative network. Experts sometimes describe this as the generative network trying to fool the discriminative network, which has to be trained to recognize particular sets of patterns and models. Scientists are looking at the potential that generative adversarial networks have to advance the power of neural networks and their ability to think in human ways.

Dear readers, next time we’ll continue this interesting topic. We hope you enjoy reading it! Thank you for your time and consideration.

Kind Regards,

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