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mafda
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Description

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

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Generative Adversarial Networks

This repository presents the basic notions that involve the concept of Generative Adversarial Networks.

"...the most interesting idea in the last 10 years in ML". Yann LeCun

Definition

Generative Adversarial Networks or GANs is a framework proposed by Ian Goodfellow, Yoshua Bengio and others in 2014.

GANs are composed of two models, represented by artificial neural network: * The first model is called a Generator and it aims to generate new data similar to the expected one. * The second model is named the Discriminator and it aims to recognize if an input data is ‘real’ — belongs to the original dataset — or if it is ‘fake’ — generated by a forger.

Read more in this post GANs — Generative Adversarial Networks 101.

Models

Definition and training some models with MNIST and CIFAR-10 datasets.

MNIST dataset

CIFAR-10 dataset

Results

Training models with Keras and TensorFlow.

MNIST dataset

Generative Adversarial Networks - GANs

A GANs implementation using fully connected layers. Notebook

| Epoch 00 | Epoch 100 | Loss | | --------------------------------- | ---------------------------------- | ----------------------------------- | | GAN with MNIST | GAN with MNIST | GAN with MNIST |

Deep Convolutional Generative Adversarial Networks - DCGANs

A DCGANs implementation using the transposed convolution technique. Notebook

| Epoch 00 | Epoch 100 | Loss | | ----------------------------------- | ------------------------------------ | ------------------------------------- | | GAN with MNIST | GAN with MNIST | GAN with MNIST |

Conditional Generative Adversarial Nets - CGANs

A CGANs implementation using fully connected layers and embedding layers. Notebook

| Epoch 00 | Epoch 100 | Loss | | ----------------------------------- | ------------------------------------ | ------------------------------------- | | CGAN with MNIST | CGAN with MNIST | CGAN with MNIST |

Context-Conditional Generative Adversarial Networks - CCGANs

A CCGANs implementation using U-Net and convolutional neural network. Notebook

| Epoch 00 | Epoch 100 | Loss | | ------------------------------------ | ------------------------------------- | -------------------------------------- | | CGAN with MNIST | CGAN with MNIST | CGAN with MNIST |

Wasserstein Generative Adversarial Networks - WGANs

A WGANs implementation using convolutional neural network. Notebook

| Epoch 00 | Epoch 100 | Loss | | ----------------------------------- | ------------------------------------ | ------------------------------------- | | WGAN with MNIST | WGAN with MNIST | WGAN with MNIST |

Least Squares General Adversarial Networks - LSGANs

A LSGANs implementation using using fully connected layers. Notebook

| Epoch 00 | Epoch 100 | Loss | | ------------------------------------------- | -------------------------------------------- | --------------------------------------------- | | LSGAN with MNIST | LSGAN with MNIST | LSGAN with MNIST |

CIFAR-10 dataset

Deep Convolutional Generative Adversarial Networks - DCGANs

A DCGANs implementation using the transposed convolution technique. Notebook

| Epoch 00 | Epoch 100 | Loss | | ---------------------------------------------- | ----------------------------------------------- | ------------------------------------------------ | | DCGAN with CIFAR-10 | DCGAN with CIFAR-10 | DCGAN with CIFAR-10 |

Conditional Generative Adversarial Networks - CGANs

A CGANs implementation using the transposed convolution and convolution neural network, and concatenate layers. Notebook

| Epoch 00 | Epoch 100 | Loss | | -------------------------------------------- | --------------------------------------------- | ---------------------------------------------- | | CGAN with CIFAR-10 | CGAN with CIFAR-10 | CGAN with CIFAR-10 |


References

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