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What Are GANs? | Generative Adversarial Networks Explained
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This video is on 'What Are GANs' will help you understand the concept of generative adversarial networks including how it works and the training phases.
Following are the topics discussed:
What are Generative models?
What are GANs?
How does GANs work?
How to Train A GAN?
GANs Applications
For any queries, You can contact me on this email id.
GANs are a framework for teaching a DL model to capture the training data’s distribution so we can generate new data from that same distribution. GANs were invented by Ian Goodfellow in 2014 and first described in the paper Generative Adversarial Nets. They are made of two distinct models, a generator and a discriminator. The job of the generator is to spawn ‘fake’ images that look like the training images. The job of the discriminator is to look at an image and output whether or not it is a real training image or a fake image from the generator. During training, the generator is constantly trying to outsmart the discriminator by generating better and better fakes, while the discriminator is working to become a better detective and correctly classify the real and fake images. The equilibrium of this game is when the generator is generating perfect fakes that look as if they came directly from the training data, and the discriminator is left to always guess at 50% confidence that the generator output is real or fake.
Join this channel to get access to perks:
#gans #generativeadversarialnetworks #neuralnetworks #ai #deeplearning #computervision #deeplearning #ml #machinelearning #pifordtechnologies #generativeai #generativemodels
Following are the topics discussed:
What are Generative models?
What are GANs?
How does GANs work?
How to Train A GAN?
GANs Applications
For any queries, You can contact me on this email id.
GANs are a framework for teaching a DL model to capture the training data’s distribution so we can generate new data from that same distribution. GANs were invented by Ian Goodfellow in 2014 and first described in the paper Generative Adversarial Nets. They are made of two distinct models, a generator and a discriminator. The job of the generator is to spawn ‘fake’ images that look like the training images. The job of the discriminator is to look at an image and output whether or not it is a real training image or a fake image from the generator. During training, the generator is constantly trying to outsmart the discriminator by generating better and better fakes, while the discriminator is working to become a better detective and correctly classify the real and fake images. The equilibrium of this game is when the generator is generating perfect fakes that look as if they came directly from the training data, and the discriminator is left to always guess at 50% confidence that the generator output is real or fake.
Join this channel to get access to perks:
#gans #generativeadversarialnetworks #neuralnetworks #ai #deeplearning #computervision #deeplearning #ml #machinelearning #pifordtechnologies #generativeai #generativemodels
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