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Generative Adversarial Networks Basic Theory of GANs (Generative Adversarial Networks) Generative Adversarial Networks GAN is one type of machine learning frameworks developed by Ian Goodfellow in 2014. They are mainly used in the generation of new data samples similar to what they were trained on, for instance, genera... | Gan ASSIGNMENT.pdf |
The two alternating training steps of GANs are as follows: 1. Training the Discriminator, (D): Two batches of the following data to which the discriminator is trained on real data samples from the training set. The discriminator is fed by these and fake data samples generated by the generator. The discriminator learns ... | Gan ASSIGNMENT.pdf |
Celeb A dataset: Celeb A dataset utilized by DCGANs where it succeeded in producing high-resolution images of celebrity faces. 2-WGAN (Wasserstein GAN) Introduction: Wasserstein GAN (WGAN) was introduced to overcome problems such as instability and mode collapse, which are commonly seen in the standard GAN models. WGA... | Gan ASSIGNMENT.pdf |
Unsupervised Learning: Unlike other GANs that require paired data, input-output pairs —Cycle GAN makes do with unpaired data, making it extremely flexible. Applications: Image style transfer: Cycle GAN can be very popularly used in the context of style transfer for various types of tasks, such as photo-to-painting con... | Gan ASSIGNMENT.pdf |
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