Deep Convolutional Generative Adversarial Network

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🎯 About :

• The architecture is comprised of two networks: a generator and a discriminator.

  • Generator: This network aims to create realistic data by generating samples from random noise. It takes random noise as input and generates data that ideally resembles the training data.

  • Discriminator: The discriminator, on the other hand, tries to distinguish between real data (from the training set) and fake data created by the generator. It's trained to correctly classify the input as either real or fake.

The two networks are trained simultaneously in a competitive process. The generator aims to create increasingly realistic data to fool the discriminator, while the discriminator aims to become more accurate in distinguishing real from fake data. Through this adversarial process, both networks improve until the generator creates data that is indistinguishable from real data.

DCGANs have been successful in generating realistic images, such as human faces or objects, and have applications in image generation, data augmentation, and other domains requiring the generation of synthetic data.

🔗 NoteBooke in Kaggel : DCGAN on Braille Images

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• Smaple Of real images

• Smaple Of Generated images

⚠️ Important note: I had previous results better than this, but I did not keep most of them because I was looking to get better results, but I was limited in using my Keggle resources (memory), so I was not able to trian it more than 700 epochs.

You can become much better than this if you have access to all the resources that help you train them, over 700 epochs.

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